{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Purpose\n",
    "Creating a network which checks the replay attack \n",
    "with sbf EKMs\n",
    "Assumptions:\n",
    "- x_lead are the EKMs from IMDs ECG signals.\n",
    "- So we use y_lead as the programmer's signal\n",
    "- 500 EKMs for each user"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports and installations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from datetime import datetime\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "import numpy as np\n",
    "import zipfile\n",
    "from pathlib import Path as path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Unzipping the 6 sbf dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the dataset path and output directories\n",
    "dataset_path = path(\"../sbf no 1_6sbf/EKMs_6sbf.zip\")\n",
    "unzip_dir = path(\"../users_zip_files_6sbf\")\n",
    "users_ekm_dir = path(\"../users_EKM_files_6sbf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Unzip the main dataset file\n",
    "if not unzip_dir.exists() and not len(list(unzip_dir.joinpath(\"Users EKM zip\").glob('*'))) == 199:\n",
    "    with zipfile.ZipFile(dataset_path, 'r') as zip_ref:\n",
    "        zip_ref.extractall(unzip_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create directory for user EKM files if it doesn't exist\n",
    "os.makedirs(users_ekm_dir, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Unzip individual user EKM zip files\n",
    "user_zip_path = os.path.join(unzip_dir, \"Users EKM zip\")\n",
    "if os.path.exists(user_zip_path):\n",
    "    for _file in os.listdir(user_zip_path):\n",
    "        file_name, _ = os.path.splitext(_file)\n",
    "        file_dir = os.path.join(users_ekm_dir, file_name)\n",
    "        \n",
    "        os.makedirs(file_dir, exist_ok=True)\n",
    "        \n",
    "        with zipfile.ZipFile(os.path.join(user_zip_path, _file), 'r') as zip_ref:\n",
    "            zip_ref.extractall(file_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Healthy EKMs check\n",
    "- Moving window checking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = f\"{users_ekm_dir}/2005/x_lead\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "EKMs = os.listdir(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def are_images_identical(image1_path, image2_path):\n",
    "    img1 = Image.open(image1_path)\n",
    "    img2 = Image.open(image2_path)\n",
    "\n",
    "    return np.array_equal(np.array(img1), np.array(img2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Images are different.\n"
     ]
    }
   ],
   "source": [
    "path_EKM1 = path + \"/\" + EKMs[0]\n",
    "path_EKM2 = path + \"/\" + EKMs[1]\n",
    "\n",
    "result = are_images_identical(path_EKM1, path_EKM2)\n",
    "print(\"Images are identical:\" if result else \"Images are different.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PlayGround"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating dictionary of list of each user's EKMs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_path = \"../users_EKM_files_6sbf\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting users id\n",
    "users_id = []\n",
    "dirs = os.listdir(dataset_path)\n",
    "for dir in dirs:\n",
    "  # if dir.startswith(\"EKM_dataset_\"):\n",
    "  user_id = dir.split(\"_\")[-1]\n",
    "  users_id.append(user_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "199"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(users_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['6sbf-ekm-no1_ekm_dataset-2005-0.png',\n",
       " '6sbf-ekm-no1_ekm_dataset-2005-1.png',\n",
       " '6sbf-ekm-no1_ekm_dataset-2005-10.png',\n",
       " '6sbf-ekm-no1_ekm_dataset-2005-100.png',\n",
       " '6sbf-ekm-no1_ekm_dataset-2005-1000.png',\n",
       " '6sbf-ekm-no1_ekm_dataset-2005-1001.png',\n",
       " '6sbf-ekm-no1_ekm_dataset-2005-1002.png',\n",
       " '6sbf-ekm-no1_ekm_dataset-2005-1003.png',\n",
       " '6sbf-ekm-no1_ekm_dataset-2005-1004.png',\n",
       " '6sbf-ekm-no1_ekm_dataset-2005-1005.png']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ! ls \"$dataset_path\"/EKM_dataset_2005/x_lead | head\n",
    "os.listdir(f\"{dataset_path}/2005/x_lead\")[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating dict of counters for each randomly-chosen users\n",
    "# of ecg 200 dataset\n",
    "# Also creating dict of users' EKMs\n",
    "ecg_200_users_EKM_amount_dict_ylead = {}\n",
    "ecg_200_users_EKMs_dict_ylead = {}\n",
    "\n",
    "for user in users_id:\n",
    "  ecg_200_users_EKM_amount_dict_ylead[user] = 0\n",
    "  ecg_200_users_EKMs_dict_ylead[user] = []\n",
    "\n",
    "# Counting each user's EKMs in dataset and collecting EKMs of him/her\n",
    "users_files = os.listdir(dataset_path)\n",
    "\n",
    "for _files in users_files:\n",
    "    user_id = _files.split(\"_\")[-1]\n",
    "    if user_id in users_id:\n",
    "      # ecg_200_users_EKM_amount_dict_ylead[user_id] = len(os.listdir(f\"{dataset_path}/EKM_dataset_{user_id}/y_lead\"))\n",
    "      # ecg_200_users_EKMs_dict_ylead[user_id] = os.listdir(f\"{dataset_path}/EKM_dataset_{user_id}/y_lead\")\n",
    "      ecg_200_users_EKM_amount_dict_ylead[user_id] = len(os.listdir(f\"{dataset_path}/{user_id}/y_lead\"))\n",
    "      ecg_200_users_EKMs_dict_ylead[user_id] = os.listdir(f\"{dataset_path}/{user_id}/y_lead\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating dict of counters for each randomly-chosen users\n",
    "# of ecg 200 dataset\n",
    "# Also creating dict of users' EKMs\n",
    "ecg_200_users_EKM_amount_dict_xlead = {}\n",
    "ecg_200_users_EKMs_dict_xlead = {}\n",
    "\n",
    "for user in users_id:\n",
    "  ecg_200_users_EKM_amount_dict_xlead[user] = 0\n",
    "  ecg_200_users_EKMs_dict_xlead[user] = []\n",
    "\n",
    "# Counting each user's EKMs in dataset and collecting EKMs of him/her\n",
    "users_files = os.listdir(dataset_path)\n",
    "\n",
    "for _files in users_files:\n",
    "    user_id = _files.split(\"_\")[-1]\n",
    "    if user_id in users_id:\n",
    "      # ecg_200_users_EKM_amount_dict_xlead[user_id] = len(os.listdir(f\"{dataset_path}/EKM_dataset_{user_id}/x_lead\"))\n",
    "      # ecg_200_users_EKMs_dict_xlead[user_id] = os.listdir(f\"{dataset_path}/EKM_dataset_{user_id}/x_lead\")\n",
    "      ecg_200_users_EKM_amount_dict_xlead[user_id] = len(os.listdir(f\"{dataset_path}/{user_id}/x_lead\"))\n",
    "      ecg_200_users_EKMs_dict_xlead[user_id] = os.listdir(f\"{dataset_path}/{user_id}/x_lead\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating dict of counters for each randomly-chosen users\n",
    "# of ecg 200 dataset\n",
    "# Also creating dict of users' EKMs\n",
    "ecg_200_users_EKM_amount_dict_zlead = {}\n",
    "ecg_200_users_EKMs_dict_zlead = {}\n",
    "\n",
    "for user in users_id:\n",
    "  ecg_200_users_EKM_amount_dict_zlead[user] = 0\n",
    "  ecg_200_users_EKMs_dict_zlead[user] = []\n",
    "\n",
    "# Counting each user's EKMs in dataset and collecting EKMs of him/her\n",
    "users_files = os.listdir(dataset_path)\n",
    "\n",
    "for _files in users_files:\n",
    "    user_id = _files.split(\"_\")[-1]\n",
    "    if user_id in users_id:\n",
    "      # ecg_200_users_EKM_amount_dict_zlead[user_id] = len(os.listdir(f\"{dataset_path}/EKM_dataset_{user_id}/z_lead\"))\n",
    "      # ecg_200_users_EKMs_dict_zlead[user_id] = os.listdir(f\"{dataset_path}/EKM_dataset_{user_id}/z_lead\")\n",
    "      ecg_200_users_EKM_amount_dict_zlead[user_id] = len(os.listdir(f\"{dataset_path}/{user_id}/z_lead\"))\n",
    "      ecg_200_users_EKMs_dict_zlead[user_id] = os.listdir(f\"{dataset_path}/{user_id}/z_lead\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3000"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(ecg_200_users_EKM_amount_dict_zlead.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3000"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(ecg_200_users_EKM_amount_dict_ylead.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3000"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(ecg_200_users_EKM_amount_dict_xlead.values())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Selecting EKMs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = []\n",
    "y = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting True labels (Same time, Same user)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecting N amount of EKMs for each user\n",
    "true_tuples = []\n",
    "\n",
    "N_each_user_ekms_amount = 500\n",
    "\n",
    "for _files in users_files:\n",
    "    user_id = _files.split(\"_\")[-1]\n",
    "    if user_id in users_id:\n",
    "    #   user_id_ekms_ylead = os.listdir(f\"{dataset_path}/EKM_dataset_{user_id}/y_lead/\")[:N_each_user_ekms_amount]\n",
    "    #   user_id_ekms_xlead = os.listdir(f\"{dataset_path}/EKM_dataset_{user_id}/x_lead/\")[:N_each_user_ekms_amount]\n",
    "\n",
    "      user_id_ekms_ylead = os.listdir(f\"{dataset_path}/{user_id}/y_lead/\")[:N_each_user_ekms_amount]\n",
    "      user_id_ekms_xlead = os.listdir(f\"{dataset_path}/{user_id}/x_lead/\")[:N_each_user_ekms_amount]\n",
    "\n",
    "      \n",
    "      for ekm_index in range(N_each_user_ekms_amount):\n",
    "        # true_tuple = (f\"{dataset_path}/EKM_dataset_{user_id}/y_lead/{user_id_ekms_ylead[ekm_index]}\", f\"{dataset_path}/EKM_dataset_{user_id}/x_lead/{user_id_ekms_xlead[ekm_index]}\")\n",
    "        true_tuple = (f\"{dataset_path}/{user_id}/y_lead/{user_id_ekms_ylead[ekm_index]}\", f\"{dataset_path}/{user_id}/x_lead/{user_id_ekms_xlead[ekm_index]}\")\n",
    "        true_tuples.append(true_tuple)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('../users_EKM_files_6sbf/10022/y_lead/6sbf-ekm-no1_ekm_dataset-10022-0.png',\n",
       "  '../users_EKM_files_6sbf/10022/x_lead/6sbf-ekm-no1_ekm_dataset-10022-0.png'),\n",
       " ('../users_EKM_files_6sbf/10022/y_lead/6sbf-ekm-no1_ekm_dataset-10022-1.png',\n",
       "  '../users_EKM_files_6sbf/10022/x_lead/6sbf-ekm-no1_ekm_dataset-10022-1.png'),\n",
       " ('../users_EKM_files_6sbf/10022/y_lead/6sbf-ekm-no1_ekm_dataset-10022-10.png',\n",
       "  '../users_EKM_files_6sbf/10022/x_lead/6sbf-ekm-no1_ekm_dataset-10022-10.png'),\n",
       " ('../users_EKM_files_6sbf/10022/y_lead/6sbf-ekm-no1_ekm_dataset-10022-100.png',\n",
       "  '../users_EKM_files_6sbf/10022/x_lead/6sbf-ekm-no1_ekm_dataset-10022-100.png'),\n",
       " ('../users_EKM_files_6sbf/10022/y_lead/6sbf-ekm-no1_ekm_dataset-10022-1000.png',\n",
       "  '../users_EKM_files_6sbf/10022/x_lead/6sbf-ekm-no1_ekm_dataset-10022-1000.png'),\n",
       " ('../users_EKM_files_6sbf/10022/y_lead/6sbf-ekm-no1_ekm_dataset-10022-1001.png',\n",
       "  '../users_EKM_files_6sbf/10022/x_lead/6sbf-ekm-no1_ekm_dataset-10022-1001.png'),\n",
       " ('../users_EKM_files_6sbf/10022/y_lead/6sbf-ekm-no1_ekm_dataset-10022-1002.png',\n",
       "  '../users_EKM_files_6sbf/10022/x_lead/6sbf-ekm-no1_ekm_dataset-10022-1002.png'),\n",
       " ('../users_EKM_files_6sbf/10022/y_lead/6sbf-ekm-no1_ekm_dataset-10022-1003.png',\n",
       "  '../users_EKM_files_6sbf/10022/x_lead/6sbf-ekm-no1_ekm_dataset-10022-1003.png'),\n",
       " ('../users_EKM_files_6sbf/10022/y_lead/6sbf-ekm-no1_ekm_dataset-10022-1004.png',\n",
       "  '../users_EKM_files_6sbf/10022/x_lead/6sbf-ekm-no1_ekm_dataset-10022-1004.png'),\n",
       " ('../users_EKM_files_6sbf/10022/y_lead/6sbf-ekm-no1_ekm_dataset-10022-1005.png',\n",
       "  '../users_EKM_files_6sbf/10022/x_lead/6sbf-ekm-no1_ekm_dataset-10022-1005.png'),\n",
       " ('../users_EKM_files_6sbf/10022/y_lead/6sbf-ekm-no1_ekm_dataset-10022-1006.png',\n",
       "  '../users_EKM_files_6sbf/10022/x_lead/6sbf-ekm-no1_ekm_dataset-10022-1006.png'),\n",
       " ('../users_EKM_files_6sbf/10022/y_lead/6sbf-ekm-no1_ekm_dataset-10022-1007.png',\n",
       "  '../users_EKM_files_6sbf/10022/x_lead/6sbf-ekm-no1_ekm_dataset-10022-1007.png'),\n",
       " ('../users_EKM_files_6sbf/10022/y_lead/6sbf-ekm-no1_ekm_dataset-10022-1008.png',\n",
       "  '../users_EKM_files_6sbf/10022/x_lead/6sbf-ekm-no1_ekm_dataset-10022-1008.png'),\n",
       " ('../users_EKM_files_6sbf/10022/y_lead/6sbf-ekm-no1_ekm_dataset-10022-1009.png',\n",
       "  '../users_EKM_files_6sbf/10022/x_lead/6sbf-ekm-no1_ekm_dataset-10022-1009.png'),\n",
       " ('../users_EKM_files_6sbf/10022/y_lead/6sbf-ekm-no1_ekm_dataset-10022-101.png',\n",
       "  '../users_EKM_files_6sbf/10022/x_lead/6sbf-ekm-no1_ekm_dataset-10022-101.png'),\n",
       " ('../users_EKM_files_6sbf/10022/y_lead/6sbf-ekm-no1_ekm_dataset-10022-1010.png',\n",
       "  '../users_EKM_files_6sbf/10022/x_lead/6sbf-ekm-no1_ekm_dataset-10022-1010.png'),\n",
       " ('../users_EKM_files_6sbf/10022/y_lead/6sbf-ekm-no1_ekm_dataset-10022-1011.png',\n",
       "  '../users_EKM_files_6sbf/10022/x_lead/6sbf-ekm-no1_ekm_dataset-10022-1011.png'),\n",
       " ('../users_EKM_files_6sbf/10022/y_lead/6sbf-ekm-no1_ekm_dataset-10022-1012.png',\n",
       "  '../users_EKM_files_6sbf/10022/x_lead/6sbf-ekm-no1_ekm_dataset-10022-1012.png'),\n",
       " ('../users_EKM_files_6sbf/10022/y_lead/6sbf-ekm-no1_ekm_dataset-10022-1013.png',\n",
       "  '../users_EKM_files_6sbf/10022/x_lead/6sbf-ekm-no1_ekm_dataset-10022-1013.png'),\n",
       " ('../users_EKM_files_6sbf/10022/y_lead/6sbf-ekm-no1_ekm_dataset-10022-1014.png',\n",
       "  '../users_EKM_files_6sbf/10022/x_lead/6sbf-ekm-no1_ekm_dataset-10022-1014.png'),\n",
       " ('../users_EKM_files_6sbf/10022/y_lead/6sbf-ekm-no1_ekm_dataset-10022-1015.png',\n",
       "  '../users_EKM_files_6sbf/10022/x_lead/6sbf-ekm-no1_ekm_dataset-10022-1015.png'),\n",
       " ('../users_EKM_files_6sbf/10022/y_lead/6sbf-ekm-no1_ekm_dataset-10022-1016.png',\n",
       "  '../users_EKM_files_6sbf/10022/x_lead/6sbf-ekm-no1_ekm_dataset-10022-1016.png'),\n",
       " ('../users_EKM_files_6sbf/10022/y_lead/6sbf-ekm-no1_ekm_dataset-10022-1017.png',\n",
       "  '../users_EKM_files_6sbf/10022/x_lead/6sbf-ekm-no1_ekm_dataset-10022-1017.png'),\n",
       " ('../users_EKM_files_6sbf/10022/y_lead/6sbf-ekm-no1_ekm_dataset-10022-1018.png',\n",
       "  '../users_EKM_files_6sbf/10022/x_lead/6sbf-ekm-no1_ekm_dataset-10022-1018.png'),\n",
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       " ('../users_EKM_files_6sbf/10023/y_lead/6sbf-ekm-no1_ekm_dataset-10023-1445.png',\n",
       "  '../users_EKM_files_6sbf/10023/x_lead/6sbf-ekm-no1_ekm_dataset-10023-1445.png'),\n",
       " ('../users_EKM_files_6sbf/10023/y_lead/6sbf-ekm-no1_ekm_dataset-10023-1446.png',\n",
       "  '../users_EKM_files_6sbf/10023/x_lead/6sbf-ekm-no1_ekm_dataset-10023-1446.png'),\n",
       " ('../users_EKM_files_6sbf/10023/y_lead/6sbf-ekm-no1_ekm_dataset-10023-1447.png',\n",
       "  '../users_EKM_files_6sbf/10023/x_lead/6sbf-ekm-no1_ekm_dataset-10023-1447.png'),\n",
       " ...]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "true_tuples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X + true_tuples\n",
    "y = y + [1 for _ in range(len(true_tuples))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting False labels (Same users, different times)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "# Function to extract the last number in the string\n",
    "def extract_last_number(file_name):\n",
    "    match = re.search(r'(\\d+)-(\\d+)\\.png$', file_name)\n",
    "    if match:\n",
    "        return int(match.group(2))\n",
    "    return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecting N amount of EKMs for each user\n",
    "false_tuples = []\n",
    "\n",
    "N_each_user_ekms_amount = 500\n",
    "\n",
    "for _files in users_files:\n",
    "    user_id = _files.split(\"_\")[-1]\n",
    "\n",
    "    if user_id in users_id:\n",
    "      # user_id_ekms_ylead = os.listdir(f\"{dataset_path}/EKM_dataset_{user_id}/y_lead/\")\n",
    "      # user_id_ekms_xlead = os.listdir(f\"{dataset_path}/EKM_dataset_{user_id}/x_lead/\")\n",
    "\n",
    "      user_id_ekms_ylead = os.listdir(f\"{dataset_path}/{user_id}/y_lead/\")\n",
    "      user_id_ekms_xlead = os.listdir(f\"{dataset_path}/{user_id}/x_lead/\")\n",
    "\n",
    "      # Sort the array based on the last serial number\n",
    "      user_id_ekms_ylead = sorted(user_id_ekms_ylead, key=extract_last_number)\n",
    "      user_id_ekms_xlead = sorted(user_id_ekms_xlead, key=extract_last_number)\n",
    "\n",
    "      for _ in range(N_each_user_ekms_amount):\n",
    "        xlead_rand_ekm = random.randint(0, ecg_200_users_EKM_amount_dict_xlead[str(user_id)] - 2)\n",
    "        ylead_rand_ekm = random.randint(xlead_rand_ekm + 1, ecg_200_users_EKM_amount_dict_ylead[str(user_id)] - 1)\n",
    "\n",
    "        # false_tuple = (f\"{dataset_path}/EKM_dataset_{user_id}/y_lead/{user_id_ekms_ylead[ylead_rand_ekm]}\", f\"{dataset_path}/EKM_dataset_{user_id}/x_lead/{user_id_ekms_xlead[xlead_rand_ekm]}\")\n",
    "        false_tuple = (f\"{dataset_path}/{user_id}/y_lead/{user_id_ekms_ylead[ylead_rand_ekm]}\", f\"{dataset_path}/{user_id}/x_lead/{user_id_ekms_xlead[xlead_rand_ekm]}\")\n",
    "        false_tuples.append(false_tuple)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
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       " ('../users_EKM_files_6sbf/10023/y_lead/6sbf-ekm-no1_ekm_dataset-10023-2345.png',\n",
       "  '../users_EKM_files_6sbf/10023/x_lead/6sbf-ekm-no1_ekm_dataset-10023-1174.png'),\n",
       " ('../users_EKM_files_6sbf/10023/y_lead/6sbf-ekm-no1_ekm_dataset-10023-2956.png',\n",
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       " ('../users_EKM_files_6sbf/10023/y_lead/6sbf-ekm-no1_ekm_dataset-10023-2715.png',\n",
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       " ('../users_EKM_files_6sbf/10023/y_lead/6sbf-ekm-no1_ekm_dataset-10023-2977.png',\n",
       "  '../users_EKM_files_6sbf/10023/x_lead/6sbf-ekm-no1_ekm_dataset-10023-2962.png'),\n",
       " ('../users_EKM_files_6sbf/10023/y_lead/6sbf-ekm-no1_ekm_dataset-10023-1385.png',\n",
       "  '../users_EKM_files_6sbf/10023/x_lead/6sbf-ekm-no1_ekm_dataset-10023-820.png'),\n",
       " ('../users_EKM_files_6sbf/10023/y_lead/6sbf-ekm-no1_ekm_dataset-10023-2784.png',\n",
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       " ('../users_EKM_files_6sbf/10023/y_lead/6sbf-ekm-no1_ekm_dataset-10023-2552.png',\n",
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       " ('../users_EKM_files_6sbf/10023/y_lead/6sbf-ekm-no1_ekm_dataset-10023-2209.png',\n",
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       " ('../users_EKM_files_6sbf/10023/y_lead/6sbf-ekm-no1_ekm_dataset-10023-1146.png',\n",
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       " ('../users_EKM_files_6sbf/10023/y_lead/6sbf-ekm-no1_ekm_dataset-10023-1206.png',\n",
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       " ('../users_EKM_files_6sbf/10023/y_lead/6sbf-ekm-no1_ekm_dataset-10023-2362.png',\n",
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       " ('../users_EKM_files_6sbf/10023/y_lead/6sbf-ekm-no1_ekm_dataset-10023-1241.png',\n",
       "  '../users_EKM_files_6sbf/10023/x_lead/6sbf-ekm-no1_ekm_dataset-10023-1210.png'),\n",
       " ('../users_EKM_files_6sbf/10023/y_lead/6sbf-ekm-no1_ekm_dataset-10023-2996.png',\n",
       "  '../users_EKM_files_6sbf/10023/x_lead/6sbf-ekm-no1_ekm_dataset-10023-2916.png'),\n",
       " ('../users_EKM_files_6sbf/10023/y_lead/6sbf-ekm-no1_ekm_dataset-10023-2140.png',\n",
       "  '../users_EKM_files_6sbf/10023/x_lead/6sbf-ekm-no1_ekm_dataset-10023-1538.png'),\n",
       " ('../users_EKM_files_6sbf/10023/y_lead/6sbf-ekm-no1_ekm_dataset-10023-1548.png',\n",
       "  '../users_EKM_files_6sbf/10023/x_lead/6sbf-ekm-no1_ekm_dataset-10023-535.png'),\n",
       " ('../users_EKM_files_6sbf/10023/y_lead/6sbf-ekm-no1_ekm_dataset-10023-2457.png',\n",
       "  '../users_EKM_files_6sbf/10023/x_lead/6sbf-ekm-no1_ekm_dataset-10023-2417.png'),\n",
       " ('../users_EKM_files_6sbf/10023/y_lead/6sbf-ekm-no1_ekm_dataset-10023-1739.png',\n",
       "  '../users_EKM_files_6sbf/10023/x_lead/6sbf-ekm-no1_ekm_dataset-10023-1612.png'),\n",
       " ('../users_EKM_files_6sbf/10023/y_lead/6sbf-ekm-no1_ekm_dataset-10023-2833.png',\n",
       "  '../users_EKM_files_6sbf/10023/x_lead/6sbf-ekm-no1_ekm_dataset-10023-448.png'),\n",
       " ('../users_EKM_files_6sbf/10023/y_lead/6sbf-ekm-no1_ekm_dataset-10023-2829.png',\n",
       "  '../users_EKM_files_6sbf/10023/x_lead/6sbf-ekm-no1_ekm_dataset-10023-712.png'),\n",
       " ('../users_EKM_files_6sbf/10023/y_lead/6sbf-ekm-no1_ekm_dataset-10023-2810.png',\n",
       "  '../users_EKM_files_6sbf/10023/x_lead/6sbf-ekm-no1_ekm_dataset-10023-2614.png'),\n",
       " ('../users_EKM_files_6sbf/10023/y_lead/6sbf-ekm-no1_ekm_dataset-10023-2529.png',\n",
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       " ('../users_EKM_files_6sbf/10023/y_lead/6sbf-ekm-no1_ekm_dataset-10023-2969.png',\n",
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       " ('../users_EKM_files_6sbf/10023/y_lead/6sbf-ekm-no1_ekm_dataset-10023-1542.png',\n",
       "  '../users_EKM_files_6sbf/10023/x_lead/6sbf-ekm-no1_ekm_dataset-10023-852.png'),\n",
       " ('../users_EKM_files_6sbf/10023/y_lead/6sbf-ekm-no1_ekm_dataset-10023-2733.png',\n",
       "  '../users_EKM_files_6sbf/10023/x_lead/6sbf-ekm-no1_ekm_dataset-10023-151.png'),\n",
       " ('../users_EKM_files_6sbf/10023/y_lead/6sbf-ekm-no1_ekm_dataset-10023-2103.png',\n",
       "  '../users_EKM_files_6sbf/10023/x_lead/6sbf-ekm-no1_ekm_dataset-10023-210.png'),\n",
       " ('../users_EKM_files_6sbf/10023/y_lead/6sbf-ekm-no1_ekm_dataset-10023-1723.png',\n",
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       " ('../users_EKM_files_6sbf/10023/y_lead/6sbf-ekm-no1_ekm_dataset-10023-789.png',\n",
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       " ('../users_EKM_files_6sbf/10023/y_lead/6sbf-ekm-no1_ekm_dataset-10023-2550.png',\n",
       "  '../users_EKM_files_6sbf/10023/x_lead/6sbf-ekm-no1_ekm_dataset-10023-2416.png'),\n",
       " ('../users_EKM_files_6sbf/10023/y_lead/6sbf-ekm-no1_ekm_dataset-10023-2910.png',\n",
       "  '../users_EKM_files_6sbf/10023/x_lead/6sbf-ekm-no1_ekm_dataset-10023-2851.png'),\n",
       " ('../users_EKM_files_6sbf/10023/y_lead/6sbf-ekm-no1_ekm_dataset-10023-2669.png',\n",
       "  '../users_EKM_files_6sbf/10023/x_lead/6sbf-ekm-no1_ekm_dataset-10023-1927.png'),\n",
       " ('../users_EKM_files_6sbf/10023/y_lead/6sbf-ekm-no1_ekm_dataset-10023-2109.png',\n",
       "  '../users_EKM_files_6sbf/10023/x_lead/6sbf-ekm-no1_ekm_dataset-10023-1095.png'),\n",
       " ('../users_EKM_files_6sbf/10023/y_lead/6sbf-ekm-no1_ekm_dataset-10023-2995.png',\n",
       "  '../users_EKM_files_6sbf/10023/x_lead/6sbf-ekm-no1_ekm_dataset-10023-2708.png'),\n",
       " ('../users_EKM_files_6sbf/10023/y_lead/6sbf-ekm-no1_ekm_dataset-10023-1399.png',\n",
       "  '../users_EKM_files_6sbf/10023/x_lead/6sbf-ekm-no1_ekm_dataset-10023-524.png'),\n",
       " ('../users_EKM_files_6sbf/10023/y_lead/6sbf-ekm-no1_ekm_dataset-10023-1468.png',\n",
       "  '../users_EKM_files_6sbf/10023/x_lead/6sbf-ekm-no1_ekm_dataset-10023-61.png'),\n",
       " ('../users_EKM_files_6sbf/10023/y_lead/6sbf-ekm-no1_ekm_dataset-10023-2466.png',\n",
       "  '../users_EKM_files_6sbf/10023/x_lead/6sbf-ekm-no1_ekm_dataset-10023-1932.png'),\n",
       " ('../users_EKM_files_6sbf/10023/y_lead/6sbf-ekm-no1_ekm_dataset-10023-1200.png',\n",
       "  '../users_EKM_files_6sbf/10023/x_lead/6sbf-ekm-no1_ekm_dataset-10023-645.png'),\n",
       " ('../users_EKM_files_6sbf/10023/y_lead/6sbf-ekm-no1_ekm_dataset-10023-1874.png',\n",
       "  '../users_EKM_files_6sbf/10023/x_lead/6sbf-ekm-no1_ekm_dataset-10023-1393.png'),\n",
       " ('../users_EKM_files_6sbf/10023/y_lead/6sbf-ekm-no1_ekm_dataset-10023-2652.png',\n",
       "  '../users_EKM_files_6sbf/10023/x_lead/6sbf-ekm-no1_ekm_dataset-10023-2539.png'),\n",
       " ...]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "false_tuples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X + false_tuples\n",
    "y = y + [0 for _ in range(len(false_tuples))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spliting train/test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting train and test data by proportion of 80/20\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = np.array(X)\n",
    "y = np.array(y)\n",
    "\n",
    "# Split the data into training and validation sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Vectorization of EKMs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def vertorizing_png_imges(address):\n",
    "  # Load the PNG image\n",
    "  image = Image.open(address)\n",
    "\n",
    "  # Convert the image to RGB mode\n",
    "  image = image.convert('RGB')\n",
    "\n",
    "  # Resize the image to match the input size expected by the CNN\n",
    "  desired_width = 31\n",
    "  desired_height = 20\n",
    "  image = image.resize((desired_width, desired_height))\n",
    "\n",
    "  # Convert the image to a NumPy array\n",
    "  image_array = np.array(image)\n",
    "\n",
    "  # Reshape the array to match the input shape expected by the CNN\n",
    "  # image_array = image_array.reshape((1, desired_height, desired_width, 3))\n",
    "\n",
    "  # Normalize the array\n",
    "  image_array = image_array.astype('float32') / 255.0\n",
    "\n",
    "  return image_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import clear_output\n",
    "\n",
    "def progress_bar(index, total_length, name_of_list):\n",
    "    bar_length = 50\n",
    "\n",
    "    # Calculate the percentage of completion\n",
    "    percent_complete = (index / total_length) * 100\n",
    "\n",
    "    # Clear the current cell's output\n",
    "    clear_output(wait=True)\n",
    "\n",
    "    print(name_of_list)\n",
    "\n",
    "    # Print the progress bar\n",
    "    print(\"[\", end=\"\")\n",
    "    completed_blocks = int(bar_length * (percent_complete / 100))\n",
    "    print(\"*\" * completed_blocks, end=\"\")\n",
    "    print(\"-\" * (bar_length - completed_blocks), end=\"]\\n\")\n",
    "\n",
    "    # Print the progress in the format: index/total_length\n",
    "    print(f\"{index}/{total_length}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def vectorizing_list_of_ekms(ekm_list, name_of_list):\n",
    "    # Vectorize a list of EKMs and return it\n",
    "    num_ekms = len(ekm_list)\n",
    "    vectorized_ekms = np.empty((num_ekms, 20, 31, 3), dtype=np.float32)\n",
    "\n",
    "    for _, ekm_path in enumerate(ekm_list):\n",
    "        veced_ekm = vertorizing_png_imges(ekm_path)\n",
    "        vectorized_ekms[_, :] = veced_ekm\n",
    "        if _ % 1000 == 0:\n",
    "            progress_bar(_, num_ekms, name_of_list)\n",
    "\n",
    "    return vectorized_ekms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No.4 => X_test_xlead\n",
      "[************************************************--]\n",
      "39000/39800\n"
     ]
    }
   ],
   "source": [
    "# Vectorizing EKMs\n",
    "X_train_xlead = [ekm[1] for ekm in X_train]\n",
    "X_train_xlead = np.array(X_train_xlead)\n",
    "X_train_xlead = vectorizing_list_of_ekms(X_train_xlead, \"No.1 => X_train_ylead\")\n",
    "\n",
    "X_train_ylead = [ekm[0] for ekm in X_train]\n",
    "X_train_ylead = np.array(X_train_ylead)\n",
    "X_train_ylead = vectorizing_list_of_ekms(X_train_ylead, \"No.2 => X_train_xlead\")\n",
    "\n",
    "\n",
    "X_test_xlead = [ekm[1] for ekm in X_test]\n",
    "X_test_xlead = np.array(X_test_xlead)\n",
    "X_test_xlead = vectorizing_list_of_ekms(X_test_xlead, \"No.3 => X_test_ylead\")\n",
    "\n",
    "X_test_ylead = [ekm[0] for ekm in X_test]\n",
    "X_test_ylead = np.array(X_test_ylead)\n",
    "X_test_ylead = vectorizing_list_of_ekms(X_test_ylead, \"No.4 => X_test_xlead\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Siamese network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from tensorflow.keras.layers import Input, Lambda\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense\n",
    "import tensorflow.keras.backend as K\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_siamese_model(input_shape, embeddingDim=54):\n",
    "  # Specify the inputs for the feature extractor network\n",
    "  inputs = Input(input_shape)\n",
    "\n",
    "  # Defining network layers\n",
    "  x = Conv2D(32, (3, 3), activation='relu', input_shape=input_shape)(inputs)\n",
    "  x = MaxPooling2D(pool_size=(2, 2))(x)\n",
    "  # x = Dropout(0.7)(x)\n",
    "\n",
    "  # Add more convolutional layer\n",
    "  x = Conv2D(64, (3, 3), activation='relu')(x)\n",
    "  # x = MaxPooling2D(pool_size=(2, 2))(x)\n",
    "  x = Dropout(0.7)(x)\n",
    "\n",
    "  x = Flatten()(x)\n",
    "  x = Dense(128, activation='relu')(x)\n",
    "  x = Dense(64, activation='relu')(x)\n",
    "  x = Dense(32, activation='relu')(x)\n",
    "  x = Dense(16, activation='relu')(x)\n",
    "  # x = Dense(8, activation='softmax')(x)\n",
    "  x = Dense(8, activation='relu')(x)\n",
    "  outputs = Dense(embeddingDim)(x)\n",
    "\n",
    "  # build the model\n",
    "  model = Model(inputs, outputs)\n",
    "\n",
    "  return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\Amihossein\\panTompkins\\bpf based in boudary EKM alpha 0.2 elements 6000\\allenv\\Lib\\site-packages\\keras\\src\\layers\\convolutional\\base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "# Input layer for the Siamese network\n",
    "input_shape = (20, 31, 3)\n",
    "input_left = Input(shape=input_shape, name='input_left')\n",
    "input_right = Input(shape=input_shape, name='input_right')\n",
    "\n",
    "# Creating the Siamese network\n",
    "featureExtractor = build_siamese_model(input_shape)\n",
    "output_left = featureExtractor(input_left)\n",
    "output_right = featureExtractor(input_right)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Custom layer to calculate the Euclidean distance between the outputs\n",
    "def euclidean_distance(vects):\n",
    "    x, y = vects\n",
    "    sum_square = K.sum(K.square(x - y), axis=1, keepdims=True)\n",
    "    return K.sqrt(K.maximum(sum_square, K.epsilon()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the Lambda layer to create a custom layer for the Euclidean distance\n",
    "distance = Lambda(euclidean_distance, output_shape=(1,))([output_left, output_right])\n",
    "\n",
    "# Creating last layer as dense layer with 1 neuron and sigmoid activation function\n",
    "outputs = Dense(1, activation=\"sigmoid\")(distance)\n",
    "\n",
    "# Creating main model\n",
    "siamese_model = Model(inputs=[input_left, input_right], outputs=outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_1\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"functional_1\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)        </span>┃<span style=\"font-weight: bold\"> Output Shape      </span>┃<span style=\"font-weight: bold\">    Param # </span>┃<span style=\"font-weight: bold\"> Connected to      </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
       "│ input_left          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">31</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                 │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ input_right         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">31</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>) │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                 │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ functional          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">54</span>)        │    <span style=\"color: #00af00; text-decoration-color: #00af00\">719,134</span> │ input_left[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>], │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Functional</span>)        │                   │            │ input_right[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ lambda (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Lambda</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)         │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ functional[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>], │\n",
       "│                     │                   │            │ functional[<span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dense_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)         │          <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span> │ lambda[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n",
       "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape     \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m   Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to     \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
       "│ input_left          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m31\u001b[0m, \u001b[38;5;34m3\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ -                 │\n",
       "│ (\u001b[38;5;33mInputLayer\u001b[0m)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ input_right         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m31\u001b[0m, \u001b[38;5;34m3\u001b[0m) │          \u001b[38;5;34m0\u001b[0m │ -                 │\n",
       "│ (\u001b[38;5;33mInputLayer\u001b[0m)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ functional          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m54\u001b[0m)        │    \u001b[38;5;34m719,134\u001b[0m │ input_left[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n",
       "│ (\u001b[38;5;33mFunctional\u001b[0m)        │                   │            │ input_right[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ lambda (\u001b[38;5;33mLambda\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)         │          \u001b[38;5;34m0\u001b[0m │ functional[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n",
       "│                     │                   │            │ functional[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dense_6 (\u001b[38;5;33mDense\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)         │          \u001b[38;5;34m2\u001b[0m │ lambda[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
       "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">719,136</span> (2.74 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m719,136\u001b[0m (2.74 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">719,136</span> (2.74 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m719,136\u001b[0m (2.74 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compile the Siamese model with the contrastive loss\n",
    "optimizer = Adam(learning_rate=0.001)\n",
    "siamese_model.compile(optimizer=optimizer, loss=\"binary_crossentropy\", metrics=['accuracy'])\n",
    "\n",
    "# Print the summary of the Siamese model\n",
    "siamese_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/60\n",
      "\u001b[1m4975/4975\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m121s\u001b[0m 24ms/step - accuracy: 0.7559 - loss: 0.4596 - val_accuracy: 0.9444 - val_loss: 0.1726\n",
      "Epoch 2/60\n",
      "\u001b[1m4975/4975\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m108s\u001b[0m 22ms/step - accuracy: 0.9408 - loss: 0.1858 - val_accuracy: 0.9527 - val_loss: 0.1307\n",
      "Epoch 3/60\n",
      "\u001b[1m4975/4975\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m108s\u001b[0m 22ms/step - accuracy: 0.9537 - loss: 0.1457 - val_accuracy: 0.9587 - val_loss: 0.1236\n",
      "Epoch 4/60\n",
      "\u001b[1m4975/4975\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m108s\u001b[0m 22ms/step - accuracy: 0.9581 - loss: 0.1330 - val_accuracy: 0.9627 - val_loss: 0.1130\n",
      "Epoch 5/60\n",
      "\u001b[1m4975/4975\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m108s\u001b[0m 22ms/step - accuracy: 0.9600 - loss: 0.1265 - val_accuracy: 0.9580 - val_loss: 0.1159\n",
      "Epoch 6/60\n",
      "\u001b[1m4975/4975\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m107s\u001b[0m 21ms/step - accuracy: 0.9620 - loss: 0.1197 - val_accuracy: 0.9604 - val_loss: 0.1107\n",
      "Epoch 7/60\n",
      "\u001b[1m4975/4975\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m108s\u001b[0m 22ms/step - accuracy: 0.9637 - loss: 0.1146 - val_accuracy: 0.9691 - val_loss: 0.1059\n",
      "Epoch 8/60\n",
      "\u001b[1m4975/4975\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m107s\u001b[0m 22ms/step - accuracy: 0.9662 - loss: 0.1086 - val_accuracy: 0.9667 - val_loss: 0.0985\n",
      "Epoch 9/60\n",
      "\u001b[1m4975/4975\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m108s\u001b[0m 22ms/step - accuracy: 0.9697 - loss: 0.0989 - val_accuracy: 0.9666 - val_loss: 0.0967\n",
      "Epoch 10/60\n",
      "\u001b[1m4975/4975\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m108s\u001b[0m 22ms/step - accuracy: 0.9707 - loss: 0.0943 - val_accuracy: 0.9663 - val_loss: 0.0955\n",
      "Epoch 11/60\n",
      "\u001b[1m4975/4975\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m109s\u001b[0m 22ms/step - accuracy: 0.9730 - loss: 0.0886 - val_accuracy: 0.9724 - val_loss: 0.0864\n",
      "Epoch 12/60\n",
      "\u001b[1m4975/4975\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m108s\u001b[0m 22ms/step - accuracy: 0.9734 - loss: 0.0858 - val_accuracy: 0.9734 - val_loss: 0.0864\n",
      "Epoch 13/60\n",
      "\u001b[1m4975/4975\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m108s\u001b[0m 22ms/step - accuracy: 0.9748 - loss: 0.0826 - val_accuracy: 0.9754 - val_loss: 0.0842\n",
      "Epoch 14/60\n",
      "\u001b[1m4975/4975\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m109s\u001b[0m 22ms/step - accuracy: 0.9753 - loss: 0.0798 - val_accuracy: 0.9703 - val_loss: 0.0869\n",
      "Epoch 15/60\n",
      "\u001b[1m4975/4975\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m109s\u001b[0m 22ms/step - accuracy: 0.9759 - loss: 0.0776 - val_accuracy: 0.9741 - val_loss: 0.0830\n",
      "Epoch 16/60\n",
      "\u001b[1m4975/4975\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m109s\u001b[0m 22ms/step - accuracy: 0.9761 - loss: 0.0759 - val_accuracy: 0.9733 - val_loss: 0.0831\n",
      "Epoch 17/60\n",
      "\u001b[1m4975/4975\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m109s\u001b[0m 22ms/step - accuracy: 0.9776 - loss: 0.0729 - val_accuracy: 0.9747 - val_loss: 0.0836\n",
      "Epoch 18/60\n",
      "\u001b[1m4975/4975\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m111s\u001b[0m 22ms/step - accuracy: 0.9773 - loss: 0.0725 - val_accuracy: 0.9716 - val_loss: 0.0859\n",
      "Epoch 19/60\n",
      "\u001b[1m4975/4975\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m111s\u001b[0m 22ms/step - accuracy: 0.9776 - loss: 0.0705 - val_accuracy: 0.9738 - val_loss: 0.0828\n",
      "Epoch 20/60\n",
      "\u001b[1m4975/4975\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m113s\u001b[0m 23ms/step - accuracy: 0.9781 - loss: 0.0703 - val_accuracy: 0.9716 - val_loss: 0.0856\n",
      "Epoch 21/60\n",
      "\u001b[1m4975/4975\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m113s\u001b[0m 23ms/step - accuracy: 0.9781 - loss: 0.0692 - val_accuracy: 0.9769 - val_loss: 0.0854\n",
      "Epoch 22/60\n",
      "\u001b[1m4975/4975\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m114s\u001b[0m 23ms/step - accuracy: 0.9798 - loss: 0.0658 - val_accuracy: 0.9722 - val_loss: 0.0845\n",
      "Epoch 23/60\n",
      "\u001b[1m4975/4975\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m113s\u001b[0m 23ms/step - accuracy: 0.9800 - loss: 0.0631 - val_accuracy: 0.9749 - val_loss: 0.0858\n",
      "Epoch 24/60\n",
      "\u001b[1m4975/4975\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m114s\u001b[0m 23ms/step - accuracy: 0.9797 - loss: 0.0633 - val_accuracy: 0.9748 - val_loss: 0.0826\n",
      "Epoch 25/60\n",
      "\u001b[1m4975/4975\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m114s\u001b[0m 23ms/step - accuracy: 0.9806 - loss: 0.0609 - val_accuracy: 0.9726 - val_loss: 0.0851\n",
      "Epoch 26/60\n",
      "\u001b[1m4975/4975\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m114s\u001b[0m 23ms/step - accuracy: 0.9814 - loss: 0.0599 - val_accuracy: 0.9752 - val_loss: 0.0847\n",
      "Epoch 27/60\n",
      "\u001b[1m4975/4975\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m114s\u001b[0m 23ms/step - accuracy: 0.9808 - loss: 0.0619 - val_accuracy: 0.9739 - val_loss: 0.0851\n",
      "Epoch 28/60\n",
      "\u001b[1m4975/4975\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m115s\u001b[0m 23ms/step - accuracy: 0.9814 - loss: 0.0578 - val_accuracy: 0.9721 - val_loss: 0.0877\n",
      "Epoch 29/60\n",
      "\u001b[1m4975/4975\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m115s\u001b[0m 23ms/step - accuracy: 0.9812 - loss: 0.0570 - val_accuracy: 0.9762 - val_loss: 0.0862\n",
      "Epoch 30/60\n",
      "\u001b[1m4975/4975\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m116s\u001b[0m 23ms/step - accuracy: 0.9818 - loss: 0.0565 - val_accuracy: 0.9762 - val_loss: 0.0847\n",
      "Epoch 31/60\n",
      "\u001b[1m4975/4975\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m116s\u001b[0m 23ms/step - accuracy: 0.9827 - loss: 0.0556 - val_accuracy: 0.9756 - val_loss: 0.0859\n",
      "Epoch 32/60\n",
      "\u001b[1m4975/4975\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m115s\u001b[0m 23ms/step - accuracy: 0.9823 - loss: 0.0551 - val_accuracy: 0.9771 - val_loss: 0.0897\n",
      "Epoch 33/60\n",
      "\u001b[1m4975/4975\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m118s\u001b[0m 24ms/step - accuracy: 0.9827 - loss: 0.0540 - val_accuracy: 0.9730 - val_loss: 0.0880\n",
      "Epoch 34/60\n",
      "\u001b[1m4975/4975\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m116s\u001b[0m 23ms/step - accuracy: 0.9826 - loss: 0.0528 - val_accuracy: 0.9771 - val_loss: 0.0871\n",
      "Epoch 35/60\n",
      "\u001b[1m4975/4975\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m116s\u001b[0m 23ms/step - accuracy: 0.9831 - loss: 0.0508 - val_accuracy: 0.9747 - val_loss: 0.0871\n",
      "Epoch 36/60\n",
      "\u001b[1m4975/4975\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m118s\u001b[0m 24ms/step - accuracy: 0.9836 - loss: 0.0494 - val_accuracy: 0.9747 - val_loss: 0.0881\n",
      "Epoch 37/60\n",
      "\u001b[1m 414/4975\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1:43\u001b[0m 23ms/step - accuracy: 0.9845 - loss: 0.0465"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[46], line 6\u001b[0m\n\u001b[0;32m      3\u001b[0m batch_size \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m32\u001b[39m\n\u001b[0;32m      5\u001b[0m \u001b[38;5;66;03m# Train the model\u001b[39;00m\n\u001b[1;32m----> 6\u001b[0m history \u001b[38;5;241m=\u001b[39m \u001b[43msiamese_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m      7\u001b[0m \u001b[43m\t\u001b[49m\u001b[43m[\u001b[49m\u001b[43mX_train_xlead\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX_train_ylead\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m      8\u001b[0m \u001b[43m\t\u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43mX_test_xlead\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX_test_ylead\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_test\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m      9\u001b[0m \u001b[43m\t\u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     10\u001b[0m \u001b[43m\t\u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mepochs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32md:\\Amihossein\\panTompkins\\bpf based in boudary EKM alpha 0.2 elements 6000\\allenv\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py:117\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    115\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    116\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 117\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    118\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    119\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[1;32md:\\Amihossein\\panTompkins\\bpf based in boudary EKM alpha 0.2 elements 6000\\allenv\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py:371\u001b[0m, in \u001b[0;36mTensorFlowTrainer.fit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq)\u001b[0m\n\u001b[0;32m    369\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m step, iterator \u001b[38;5;129;01min\u001b[39;00m epoch_iterator:\n\u001b[0;32m    370\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_begin(step)\n\u001b[1;32m--> 371\u001b[0m     logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    372\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_end(step, logs)\n\u001b[0;32m    373\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstop_training:\n",
      "File \u001b[1;32md:\\Amihossein\\panTompkins\\bpf based in boudary EKM alpha 0.2 elements 6000\\allenv\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py:219\u001b[0m, in \u001b[0;36mTensorFlowTrainer._make_function.<locals>.function\u001b[1;34m(iterator)\u001b[0m\n\u001b[0;32m    215\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mfunction\u001b[39m(iterator):\n\u001b[0;32m    216\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\n\u001b[0;32m    217\u001b[0m         iterator, (tf\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mIterator, tf\u001b[38;5;241m.\u001b[39mdistribute\u001b[38;5;241m.\u001b[39mDistributedIterator)\n\u001b[0;32m    218\u001b[0m     ):\n\u001b[1;32m--> 219\u001b[0m         opt_outputs \u001b[38;5;241m=\u001b[39m \u001b[43mmulti_step_on_iterator\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    220\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m opt_outputs\u001b[38;5;241m.\u001b[39mhas_value():\n\u001b[0;32m    221\u001b[0m             \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m\n",
      "File \u001b[1;32md:\\Amihossein\\panTompkins\\bpf based in boudary EKM alpha 0.2 elements 6000\\allenv\\Lib\\site-packages\\tensorflow\\python\\util\\traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 150\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    152\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[1;32md:\\Amihossein\\panTompkins\\bpf based in boudary EKM alpha 0.2 elements 6000\\allenv\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\polymorphic_function.py:833\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    830\u001b[0m compiler \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxla\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnonXla\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    832\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OptionalXlaContext(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile):\n\u001b[1;32m--> 833\u001b[0m   result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    835\u001b[0m new_tracing_count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexperimental_get_tracing_count()\n\u001b[0;32m    836\u001b[0m without_tracing \u001b[38;5;241m=\u001b[39m (tracing_count \u001b[38;5;241m==\u001b[39m new_tracing_count)\n",
      "File \u001b[1;32md:\\Amihossein\\panTompkins\\bpf based in boudary EKM alpha 0.2 elements 6000\\allenv\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\polymorphic_function.py:878\u001b[0m, in \u001b[0;36mFunction._call\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    875\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[0;32m    876\u001b[0m \u001b[38;5;66;03m# In this case we have not created variables on the first call. So we can\u001b[39;00m\n\u001b[0;32m    877\u001b[0m \u001b[38;5;66;03m# run the first trace but we should fail if variables are created.\u001b[39;00m\n\u001b[1;32m--> 878\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[43mtracing_compilation\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    879\u001b[0m \u001b[43m    \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_variable_creation_config\u001b[49m\n\u001b[0;32m    880\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    881\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_created_variables:\n\u001b[0;32m    882\u001b[0m   \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCreating variables on a non-first call to a function\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    883\u001b[0m                    \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m decorated with tf.function.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[1;32md:\\Amihossein\\panTompkins\\bpf based in boudary EKM alpha 0.2 elements 6000\\allenv\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\tracing_compilation.py:139\u001b[0m, in \u001b[0;36mcall_function\u001b[1;34m(args, kwargs, tracing_options)\u001b[0m\n\u001b[0;32m    137\u001b[0m bound_args \u001b[38;5;241m=\u001b[39m function\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mbind(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    138\u001b[0m flat_inputs \u001b[38;5;241m=\u001b[39m function\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39munpack_inputs(bound_args)\n\u001b[1;32m--> 139\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_flat\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# pylint: disable=protected-access\u001b[39;49;00m\n\u001b[0;32m    140\u001b[0m \u001b[43m    \u001b[49m\u001b[43mflat_inputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcaptured_inputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfunction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcaptured_inputs\u001b[49m\n\u001b[0;32m    141\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32md:\\Amihossein\\panTompkins\\bpf based in boudary EKM alpha 0.2 elements 6000\\allenv\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\concrete_function.py:1322\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[1;34m(self, tensor_inputs, captured_inputs)\u001b[0m\n\u001b[0;32m   1318\u001b[0m possible_gradient_type \u001b[38;5;241m=\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[0;32m   1319\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (possible_gradient_type \u001b[38;5;241m==\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[0;32m   1320\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m executing_eagerly):\n\u001b[0;32m   1321\u001b[0m   \u001b[38;5;66;03m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[1;32m-> 1322\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_inference_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_preflattened\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1323\u001b[0m forward_backward \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[0;32m   1324\u001b[0m     args,\n\u001b[0;32m   1325\u001b[0m     possible_gradient_type,\n\u001b[0;32m   1326\u001b[0m     executing_eagerly)\n\u001b[0;32m   1327\u001b[0m forward_function, args_with_tangents \u001b[38;5;241m=\u001b[39m forward_backward\u001b[38;5;241m.\u001b[39mforward()\n",
      "File \u001b[1;32md:\\Amihossein\\panTompkins\\bpf based in boudary EKM alpha 0.2 elements 6000\\allenv\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\atomic_function.py:216\u001b[0m, in \u001b[0;36mAtomicFunction.call_preflattened\u001b[1;34m(self, args)\u001b[0m\n\u001b[0;32m    214\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mcall_preflattened\u001b[39m(\u001b[38;5;28mself\u001b[39m, args: Sequence[core\u001b[38;5;241m.\u001b[39mTensor]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[0;32m    215\u001b[0m \u001b[38;5;250m  \u001b[39m\u001b[38;5;124;03m\"\"\"Calls with flattened tensor inputs and returns the structured output.\"\"\"\u001b[39;00m\n\u001b[1;32m--> 216\u001b[0m   flat_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_flat\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    217\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mpack_output(flat_outputs)\n",
      "File \u001b[1;32md:\\Amihossein\\panTompkins\\bpf based in boudary EKM alpha 0.2 elements 6000\\allenv\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\atomic_function.py:251\u001b[0m, in \u001b[0;36mAtomicFunction.call_flat\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m    249\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m record\u001b[38;5;241m.\u001b[39mstop_recording():\n\u001b[0;32m    250\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mexecuting_eagerly():\n\u001b[1;32m--> 251\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_bound_context\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    252\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    253\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    254\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunction_type\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mflat_outputs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    255\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    256\u001b[0m   \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    257\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m make_call_op_in_graph(\n\u001b[0;32m    258\u001b[0m         \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m    259\u001b[0m         \u001b[38;5;28mlist\u001b[39m(args),\n\u001b[0;32m    260\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mfunction_call_options\u001b[38;5;241m.\u001b[39mas_attrs(),\n\u001b[0;32m    261\u001b[0m     )\n",
      "File \u001b[1;32md:\\Amihossein\\panTompkins\\bpf based in boudary EKM alpha 0.2 elements 6000\\allenv\\Lib\\site-packages\\tensorflow\\python\\eager\\context.py:1683\u001b[0m, in \u001b[0;36mContext.call_function\u001b[1;34m(self, name, tensor_inputs, num_outputs)\u001b[0m\n\u001b[0;32m   1681\u001b[0m cancellation_context \u001b[38;5;241m=\u001b[39m cancellation\u001b[38;5;241m.\u001b[39mcontext()\n\u001b[0;32m   1682\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cancellation_context \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1683\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m \u001b[43mexecute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1684\u001b[0m \u001b[43m      \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mutf-8\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1685\u001b[0m \u001b[43m      \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1686\u001b[0m \u001b[43m      \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtensor_inputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1687\u001b[0m \u001b[43m      \u001b[49m\u001b[43mattrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1688\u001b[0m \u001b[43m      \u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1689\u001b[0m \u001b[43m  \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1690\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1691\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute_with_cancellation(\n\u001b[0;32m   1692\u001b[0m       name\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[0;32m   1693\u001b[0m       num_outputs\u001b[38;5;241m=\u001b[39mnum_outputs,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1697\u001b[0m       cancellation_manager\u001b[38;5;241m=\u001b[39mcancellation_context,\n\u001b[0;32m   1698\u001b[0m   )\n",
      "File \u001b[1;32md:\\Amihossein\\panTompkins\\bpf based in boudary EKM alpha 0.2 elements 6000\\allenv\\Lib\\site-packages\\tensorflow\\python\\eager\\execute.py:53\u001b[0m, in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     51\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m     52\u001b[0m   ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[1;32m---> 53\u001b[0m   tensors \u001b[38;5;241m=\u001b[39m \u001b[43mpywrap_tfe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTFE_Py_Execute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_handle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     54\u001b[0m \u001b[43m                                      \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     55\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m     56\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# Training parameters\n",
    "epochs = 60\n",
    "batch_size = 32\n",
    "\n",
    "# Train the model\n",
    "history = siamese_model.fit(\n",
    "\t[X_train_xlead, X_train_ylead], y_train,\n",
    "\tvalidation_data=([X_test_xlead, X_test_ylead], y_test),\n",
    "\tbatch_size=batch_size,\n",
    "\tepochs=epochs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accuracy, loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot the training and validation loss\n",
    "plt.plot(history.history['loss'], label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "plt.title('Model Loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss, accuracy = siamese_model.evaluate([X_test_xlead, X_test_ylead], y_test)\n",
    "print(f\"Test Loss: {loss}\")\n",
    "print(f\"Test Accuracy: {accuracy}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pre = siamese_model.predict([X_test_xlead, X_test_ylead])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Threshold finder\n",
    "positive_pre = []\n",
    "negative_pre = []\n",
    "\n",
    "for pred, actual in zip(pre, y_test):\n",
    "    if actual == 1: \n",
    "        positive_pre.extend(pred)\n",
    "    elif actual == 0:\n",
    "        negative_pre.extend(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9765555362615326"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(positive_pre) / len(positive_pre)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.03651785180328671"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(negative_pre) / len(negative_pre)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "lower_treshhold = 0.90\n",
    "\n",
    "model_output = []\n",
    "for index in range(len(y_test)):\n",
    "  if pre[index] >= lower_treshhold:\n",
    "    model_output.append(1)\n",
    "  else:\n",
    "      model_output.append(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "tp = 0\n",
    "tn = 0\n",
    "fp = 0\n",
    "fn = 0\n",
    "\n",
    "for index in range(len(model_output)):\n",
    "  if model_output[index] == 1 and y_test[index] == 1:\n",
    "      tp += 1\n",
    "  if model_output[index] == 1 and y_test[index] == 0:\n",
    "    fp += 1\n",
    "  if model_output[index] == 0 and y_test[index] == 0:\n",
    "    tn += 1\n",
    "  if model_output[index] == 0 and y_test[index] == 1:\n",
    "    fn += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9764070351758793"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc = (tp + tn) / len(y_test)\n",
    "acc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AUPR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_recall_curve, auc\n",
    "from tensorflow.keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_aupr(y_true, y_pred_probs):\n",
    "    \"\"\"\n",
    "    Calculate the Area Under the Precision-Recall Curve (AUPR).\n",
    "    \"\"\"\n",
    "    precision, recall, _ = precision_recall_curve(y_true.ravel(), y_pred_probs.ravel())\n",
    "    aupr = auc(recall, precision)\n",
    "    return aupr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUPR: 0.9823\n"
     ]
    }
   ],
   "source": [
    "y_pred_probs = np.array([[1, 0] if pred == 0 else [0, 1] for pred in model_output])\n",
    "y_test_onehot = to_categorical(y_test, num_classes=2)\n",
    "\n",
    "aupr = calculate_aupr(y_test_onehot, y_pred_probs)\n",
    "print(f\"AUPR: {aupr:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AUC-ROC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "def calculate_auc_roc(y_true, y_pred_probs):\n",
    "    \"\"\"\n",
    "    Calculate the Area Under the Receiver Operating Characteristic Curve (AUC-ROC).\n",
    "    \"\"\"\n",
    "    auc_roc = roc_auc_score(y_true, y_pred_probs, multi_class='ovr')  # 'ovr' for one-vs-rest\n",
    "    return auc_roc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC-ROC: 0.9764\n"
     ]
    }
   ],
   "source": [
    "y_pred_probs = np.array(model_output)\n",
    "auc_roc = calculate_auc_roc(y_test, y_pred_probs)\n",
    "print(f\"AUC-ROC: {auc_roc:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "def calculate_confusion_matrix(y_true, y_pred):\n",
    "    \"\"\"\n",
    "    Calculate the confusion matrix.\n",
    "    \"\"\"\n",
    "    cm = confusion_matrix(y_true, y_pred)\n",
    "    return cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred_probs = np.array(model_output)\n",
    "# y_pred = np.argmax(y_pred_probs, axis=1)\n",
    "cm = calculate_confusion_matrix(y_test, y_pred_probs)\n",
    "class_names = 2\n",
    " \n",
    "# Create a heatmap\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(cm, annot=True, cmap='Blues', fmt='.2f', xticklabels=class_names, yticklabels=class_names)\n",
    "plt.title('Normalized Confusion Matrix')\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.ylabel('True Label')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Saving the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sadeghi/.local/lib/python3.8/site-packages/keras/src/engine/training.py:3000: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  saving_api.save_model(\n"
     ]
    }
   ],
   "source": [
    "# Save the model in HDF5 format\n",
    "siamese_model.save(\"../6sbf_replayCheck.h5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import load_model\n",
    "\n",
    "# Load the HDF5 model\n",
    "siamese_model = load_model(\"../6sbf_replayCheck.h5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 10 fold validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedKFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_splits = 10\n",
    "skf = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Metrics to store performance\n",
    "folds_evaluation = {}\n",
    "fold_accuracies = []\n",
    "fold_accuracies_with_treshold = []\n",
    "\n",
    "for index in range(n_splits):\n",
    "    folds_evaluation[index] = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No.2 => y lead\n",
      "[*************************************************-]\n",
      "198999/199000\n"
     ]
    }
   ],
   "source": [
    "# Vectorizing EKMs\n",
    "X_xlead = [ekm[1] for ekm in X]\n",
    "X_xlead = np.array(X_xlead)\n",
    "X_xlead = vectorizing_list_of_ekms(X_xlead, \"No.1 => x lead\")\n",
    "\n",
    "X_ylead = [ekm[0] for ekm in X]\n",
    "X_ylead = np.array(X_ylead)\n",
    "X_ylead = vectorizing_list_of_ekms(X_ylead, \"No.2 => y lead\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/60\n",
      "1400/1400 [==============================] - 30s 20ms/step - loss: 0.4231 - accuracy: 0.8306 - val_loss: 0.2653 - val_accuracy: 0.9408\n",
      "Epoch 2/60\n",
      "1400/1400 [==============================] - 27s 19ms/step - loss: 0.2461 - accuracy: 0.9417 - val_loss: 0.1938 - val_accuracy: 0.9428\n",
      "Epoch 3/60\n",
      "1400/1400 [==============================] - 27s 19ms/step - loss: 0.1995 - accuracy: 0.9431 - val_loss: 0.1701 - val_accuracy: 0.9447\n",
      "Epoch 4/60\n",
      "1400/1400 [==============================] - 27s 19ms/step - loss: 0.1835 - accuracy: 0.9438 - val_loss: 0.1690 - val_accuracy: 0.9442\n",
      "Epoch 5/60\n",
      "1400/1400 [==============================] - 27s 19ms/step - loss: 0.1758 - accuracy: 0.9452 - val_loss: 0.1703 - val_accuracy: 0.9410\n",
      "Epoch 6/60\n",
      "1400/1400 [==============================] - 27s 19ms/step - loss: 0.1718 - accuracy: 0.9458 - val_loss: 0.1638 - val_accuracy: 0.9457\n",
      "Epoch 7/60\n",
      "1400/1400 [==============================] - 27s 19ms/step - loss: 0.1535 - accuracy: 0.9532 - val_loss: 0.1302 - val_accuracy: 0.9559\n",
      "Epoch 8/60\n",
      "1400/1400 [==============================] - 26s 19ms/step - loss: 0.1281 - accuracy: 0.9611 - val_loss: 0.1268 - val_accuracy: 0.9620\n",
      "Epoch 9/60\n",
      "1400/1400 [==============================] - 27s 19ms/step - loss: 0.1232 - accuracy: 0.9624 - val_loss: 0.1230 - val_accuracy: 0.9605\n",
      "Epoch 10/60\n",
      "1400/1400 [==============================] - 27s 19ms/step - loss: 0.1194 - accuracy: 0.9638 - val_loss: 0.1244 - val_accuracy: 0.9638\n",
      "Epoch 11/60\n",
      "1400/1400 [==============================] - 27s 19ms/step - loss: 0.1159 - accuracy: 0.9648 - val_loss: 0.1226 - val_accuracy: 0.9601\n",
      "Epoch 12/60\n",
      "1400/1400 [==============================] - 26s 19ms/step - loss: 0.1126 - accuracy: 0.9659 - val_loss: 0.1212 - val_accuracy: 0.9623\n",
      "Epoch 13/60\n",
      "1400/1400 [==============================] - 26s 19ms/step - loss: 0.1102 - accuracy: 0.9662 - val_loss: 0.1233 - val_accuracy: 0.9632\n",
      "Epoch 14/60\n",
      "1400/1400 [==============================] - 26s 19ms/step - loss: 0.1077 - accuracy: 0.9667 - val_loss: 0.1242 - val_accuracy: 0.9623\n",
      "Epoch 15/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.1060 - accuracy: 0.9673 - val_loss: 0.1260 - val_accuracy: 0.9599\n",
      "Epoch 16/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.1038 - accuracy: 0.9681 - val_loss: 0.1278 - val_accuracy: 0.9577\n",
      "Epoch 17/60\n",
      "1400/1400 [==============================] - 26s 19ms/step - loss: 0.1015 - accuracy: 0.9687 - val_loss: 0.1278 - val_accuracy: 0.9600\n",
      "Epoch 18/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.1008 - accuracy: 0.9688 - val_loss: 0.1244 - val_accuracy: 0.9610\n",
      "Epoch 19/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0980 - accuracy: 0.9700 - val_loss: 0.1266 - val_accuracy: 0.9617\n",
      "Epoch 20/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0961 - accuracy: 0.9701 - val_loss: 0.1300 - val_accuracy: 0.9595\n",
      "Epoch 21/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0951 - accuracy: 0.9704 - val_loss: 0.1301 - val_accuracy: 0.9585\n",
      "Epoch 22/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0935 - accuracy: 0.9711 - val_loss: 0.1316 - val_accuracy: 0.9594\n",
      "Epoch 23/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0923 - accuracy: 0.9712 - val_loss: 0.1306 - val_accuracy: 0.9616\n",
      "Epoch 24/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0898 - accuracy: 0.9720 - val_loss: 0.1337 - val_accuracy: 0.9599\n",
      "Epoch 25/60\n",
      "1400/1400 [==============================] - 26s 19ms/step - loss: 0.0890 - accuracy: 0.9724 - val_loss: 0.1298 - val_accuracy: 0.9601\n",
      "Epoch 26/60\n",
      "1400/1400 [==============================] - 26s 19ms/step - loss: 0.0878 - accuracy: 0.9723 - val_loss: 0.1329 - val_accuracy: 0.9614\n",
      "Epoch 27/60\n",
      "1400/1400 [==============================] - 26s 19ms/step - loss: 0.0873 - accuracy: 0.9727 - val_loss: 0.1338 - val_accuracy: 0.9626\n",
      "Epoch 28/60\n",
      "1400/1400 [==============================] - 26s 19ms/step - loss: 0.0857 - accuracy: 0.9725 - val_loss: 0.1335 - val_accuracy: 0.9616\n",
      "Epoch 29/60\n",
      "1400/1400 [==============================] - 26s 19ms/step - loss: 0.0838 - accuracy: 0.9732 - val_loss: 0.1354 - val_accuracy: 0.9627\n",
      "Epoch 30/60\n",
      "1400/1400 [==============================] - 26s 19ms/step - loss: 0.0821 - accuracy: 0.9746 - val_loss: 0.1352 - val_accuracy: 0.9616\n",
      "Epoch 31/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0816 - accuracy: 0.9737 - val_loss: 0.1372 - val_accuracy: 0.9606\n",
      "Epoch 32/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0801 - accuracy: 0.9747 - val_loss: 0.1321 - val_accuracy: 0.9620\n",
      "Epoch 33/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0789 - accuracy: 0.9751 - val_loss: 0.1369 - val_accuracy: 0.9624\n",
      "Epoch 34/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0772 - accuracy: 0.9752 - val_loss: 0.1347 - val_accuracy: 0.9631\n",
      "Epoch 35/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0766 - accuracy: 0.9753 - val_loss: 0.1312 - val_accuracy: 0.9637\n",
      "Epoch 36/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0740 - accuracy: 0.9761 - val_loss: 0.1285 - val_accuracy: 0.9649\n",
      "Epoch 37/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0710 - accuracy: 0.9774 - val_loss: 0.1325 - val_accuracy: 0.9571\n",
      "Epoch 38/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0704 - accuracy: 0.9775 - val_loss: 0.1283 - val_accuracy: 0.9648\n",
      "Epoch 39/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0685 - accuracy: 0.9779 - val_loss: 0.1265 - val_accuracy: 0.9654\n",
      "Epoch 40/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0670 - accuracy: 0.9781 - val_loss: 0.1271 - val_accuracy: 0.9649\n",
      "Epoch 41/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0662 - accuracy: 0.9788 - val_loss: 0.1288 - val_accuracy: 0.9659\n",
      "Epoch 42/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0649 - accuracy: 0.9787 - val_loss: 0.1264 - val_accuracy: 0.9683\n",
      "Epoch 43/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0647 - accuracy: 0.9787 - val_loss: 0.1263 - val_accuracy: 0.9665\n",
      "Epoch 44/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0626 - accuracy: 0.9794 - val_loss: 0.1299 - val_accuracy: 0.9637\n",
      "Epoch 45/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0623 - accuracy: 0.9793 - val_loss: 0.1272 - val_accuracy: 0.9654\n",
      "Epoch 46/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0599 - accuracy: 0.9804 - val_loss: 0.1301 - val_accuracy: 0.9658\n",
      "Epoch 47/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0605 - accuracy: 0.9802 - val_loss: 0.1304 - val_accuracy: 0.9672\n",
      "Epoch 48/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0597 - accuracy: 0.9802 - val_loss: 0.1301 - val_accuracy: 0.9649\n",
      "Epoch 49/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0583 - accuracy: 0.9807 - val_loss: 0.1302 - val_accuracy: 0.9668\n",
      "Epoch 50/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0578 - accuracy: 0.9806 - val_loss: 0.1288 - val_accuracy: 0.9653\n",
      "Epoch 51/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0570 - accuracy: 0.9812 - val_loss: 0.1324 - val_accuracy: 0.9664\n",
      "Epoch 52/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0558 - accuracy: 0.9814 - val_loss: 0.1354 - val_accuracy: 0.9640\n",
      "Epoch 53/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0556 - accuracy: 0.9813 - val_loss: 0.1335 - val_accuracy: 0.9688\n",
      "Epoch 54/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0552 - accuracy: 0.9817 - val_loss: 0.1351 - val_accuracy: 0.9670\n",
      "Epoch 55/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0542 - accuracy: 0.9821 - val_loss: 0.1340 - val_accuracy: 0.9643\n",
      "Epoch 56/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0531 - accuracy: 0.9824 - val_loss: 0.1338 - val_accuracy: 0.9677\n",
      "Epoch 57/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0531 - accuracy: 0.9821 - val_loss: 0.1341 - val_accuracy: 0.9656\n",
      "Epoch 58/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0532 - accuracy: 0.9824 - val_loss: 0.1347 - val_accuracy: 0.9655\n",
      "Epoch 59/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0519 - accuracy: 0.9825 - val_loss: 0.1394 - val_accuracy: 0.9674\n",
      "Epoch 60/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0509 - accuracy: 0.9827 - val_loss: 0.1364 - val_accuracy: 0.9673\n",
      "622/622 - 2s - loss: 0.1364 - accuracy: 0.9673 - 2s/epoch - 3ms/step\n",
      "622/622 [==============================] - 2s 3ms/step\n",
      "Completed fold 1/10\n",
      "Epoch 1/60\n",
      "1400/1400 [==============================] - 29s 19ms/step - loss: 0.0641 - accuracy: 0.9798 - val_loss: 0.0389 - val_accuracy: 0.9844\n",
      "Epoch 2/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0602 - accuracy: 0.9804 - val_loss: 0.0337 - val_accuracy: 0.9872\n",
      "Epoch 3/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0598 - accuracy: 0.9807 - val_loss: 0.0384 - val_accuracy: 0.9858\n",
      "Epoch 4/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0592 - accuracy: 0.9807 - val_loss: 0.0397 - val_accuracy: 0.9843\n",
      "Epoch 5/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0577 - accuracy: 0.9814 - val_loss: 0.0372 - val_accuracy: 0.9858\n",
      "Epoch 6/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0570 - accuracy: 0.9811 - val_loss: 0.0406 - val_accuracy: 0.9844\n",
      "Epoch 7/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0547 - accuracy: 0.9818 - val_loss: 0.0408 - val_accuracy: 0.9848\n",
      "Epoch 8/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0538 - accuracy: 0.9823 - val_loss: 0.0446 - val_accuracy: 0.9821\n",
      "Epoch 9/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0537 - accuracy: 0.9824 - val_loss: 0.0431 - val_accuracy: 0.9838\n",
      "Epoch 10/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0535 - accuracy: 0.9821 - val_loss: 0.0476 - val_accuracy: 0.9825\n",
      "Epoch 11/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0527 - accuracy: 0.9825 - val_loss: 0.0472 - val_accuracy: 0.9822\n",
      "Epoch 12/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0516 - accuracy: 0.9829 - val_loss: 0.0488 - val_accuracy: 0.9821\n",
      "Epoch 13/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0503 - accuracy: 0.9831 - val_loss: 0.0490 - val_accuracy: 0.9818\n",
      "Epoch 14/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0494 - accuracy: 0.9837 - val_loss: 0.0507 - val_accuracy: 0.9807\n",
      "Epoch 15/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0488 - accuracy: 0.9835 - val_loss: 0.0515 - val_accuracy: 0.9813\n",
      "Epoch 16/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0482 - accuracy: 0.9838 - val_loss: 0.0523 - val_accuracy: 0.9807\n",
      "Epoch 17/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0481 - accuracy: 0.9837 - val_loss: 0.0550 - val_accuracy: 0.9801\n",
      "Epoch 18/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0472 - accuracy: 0.9842 - val_loss: 0.0544 - val_accuracy: 0.9812\n",
      "Epoch 19/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0468 - accuracy: 0.9842 - val_loss: 0.0528 - val_accuracy: 0.9815\n",
      "Epoch 20/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0454 - accuracy: 0.9850 - val_loss: 0.0577 - val_accuracy: 0.9788\n",
      "Epoch 21/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0452 - accuracy: 0.9845 - val_loss: 0.0568 - val_accuracy: 0.9792\n",
      "Epoch 22/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0458 - accuracy: 0.9844 - val_loss: 0.0590 - val_accuracy: 0.9791\n",
      "Epoch 23/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0437 - accuracy: 0.9852 - val_loss: 0.0613 - val_accuracy: 0.9782\n",
      "Epoch 24/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0442 - accuracy: 0.9847 - val_loss: 0.0581 - val_accuracy: 0.9796\n",
      "Epoch 25/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0431 - accuracy: 0.9853 - val_loss: 0.0580 - val_accuracy: 0.9800\n",
      "Epoch 26/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0434 - accuracy: 0.9850 - val_loss: 0.0679 - val_accuracy: 0.9760\n",
      "Epoch 27/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0429 - accuracy: 0.9852 - val_loss: 0.0629 - val_accuracy: 0.9787\n",
      "Epoch 28/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0423 - accuracy: 0.9856 - val_loss: 0.0645 - val_accuracy: 0.9775\n",
      "Epoch 29/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0418 - accuracy: 0.9857 - val_loss: 0.0611 - val_accuracy: 0.9794\n",
      "Epoch 30/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0408 - accuracy: 0.9861 - val_loss: 0.0638 - val_accuracy: 0.9771\n",
      "Epoch 31/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0411 - accuracy: 0.9859 - val_loss: 0.0660 - val_accuracy: 0.9776\n",
      "Epoch 32/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0402 - accuracy: 0.9859 - val_loss: 0.0633 - val_accuracy: 0.9782\n",
      "Epoch 33/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0405 - accuracy: 0.9859 - val_loss: 0.0653 - val_accuracy: 0.9788\n",
      "Epoch 34/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0395 - accuracy: 0.9864 - val_loss: 0.0647 - val_accuracy: 0.9787\n",
      "Epoch 35/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0385 - accuracy: 0.9870 - val_loss: 0.0663 - val_accuracy: 0.9793\n",
      "Epoch 36/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0398 - accuracy: 0.9860 - val_loss: 0.0674 - val_accuracy: 0.9777\n",
      "Epoch 37/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0391 - accuracy: 0.9864 - val_loss: 0.0694 - val_accuracy: 0.9776\n",
      "Epoch 38/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0377 - accuracy: 0.9868 - val_loss: 0.0699 - val_accuracy: 0.9775\n",
      "Epoch 39/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0379 - accuracy: 0.9867 - val_loss: 0.0710 - val_accuracy: 0.9761\n",
      "Epoch 40/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0372 - accuracy: 0.9867 - val_loss: 0.0715 - val_accuracy: 0.9773\n",
      "Epoch 41/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0365 - accuracy: 0.9873 - val_loss: 0.0714 - val_accuracy: 0.9790\n",
      "Epoch 42/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0359 - accuracy: 0.9875 - val_loss: 0.0732 - val_accuracy: 0.9770\n",
      "Epoch 43/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0373 - accuracy: 0.9870 - val_loss: 0.0701 - val_accuracy: 0.9781\n",
      "Epoch 44/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0359 - accuracy: 0.9876 - val_loss: 0.0729 - val_accuracy: 0.9781\n",
      "Epoch 45/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0353 - accuracy: 0.9875 - val_loss: 0.0731 - val_accuracy: 0.9767\n",
      "Epoch 46/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0359 - accuracy: 0.9873 - val_loss: 0.0749 - val_accuracy: 0.9760\n",
      "Epoch 47/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0353 - accuracy: 0.9879 - val_loss: 0.0776 - val_accuracy: 0.9762\n",
      "Epoch 48/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0351 - accuracy: 0.9877 - val_loss: 0.0794 - val_accuracy: 0.9751\n",
      "Epoch 49/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0339 - accuracy: 0.9881 - val_loss: 0.0792 - val_accuracy: 0.9752\n",
      "Epoch 50/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0344 - accuracy: 0.9879 - val_loss: 0.0800 - val_accuracy: 0.9763\n",
      "Epoch 51/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0332 - accuracy: 0.9882 - val_loss: 0.0783 - val_accuracy: 0.9769\n",
      "Epoch 52/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0337 - accuracy: 0.9880 - val_loss: 0.0773 - val_accuracy: 0.9763\n",
      "Epoch 53/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0337 - accuracy: 0.9879 - val_loss: 0.0758 - val_accuracy: 0.9771\n",
      "Epoch 54/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0335 - accuracy: 0.9880 - val_loss: 0.0807 - val_accuracy: 0.9771\n",
      "Epoch 55/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0330 - accuracy: 0.9883 - val_loss: 0.0799 - val_accuracy: 0.9758\n",
      "Epoch 56/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0330 - accuracy: 0.9883 - val_loss: 0.0804 - val_accuracy: 0.9760\n",
      "Epoch 57/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0323 - accuracy: 0.9884 - val_loss: 0.0798 - val_accuracy: 0.9752\n",
      "Epoch 58/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0318 - accuracy: 0.9885 - val_loss: 0.0801 - val_accuracy: 0.9762\n",
      "Epoch 59/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0319 - accuracy: 0.9886 - val_loss: 0.0801 - val_accuracy: 0.9760\n",
      "Epoch 60/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0320 - accuracy: 0.9885 - val_loss: 0.0824 - val_accuracy: 0.9759\n",
      "622/622 - 2s - loss: 0.0824 - accuracy: 0.9759 - 2s/epoch - 3ms/step\n",
      "622/622 [==============================] - 2s 3ms/step\n",
      "Completed fold 2/10\n",
      "Epoch 1/60\n",
      "1400/1400 [==============================] - 28s 18ms/step - loss: 0.0404 - accuracy: 0.9859 - val_loss: 0.0134 - val_accuracy: 0.9961\n",
      "Epoch 2/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0387 - accuracy: 0.9870 - val_loss: 0.0149 - val_accuracy: 0.9942\n",
      "Epoch 3/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0372 - accuracy: 0.9874 - val_loss: 0.0172 - val_accuracy: 0.9941\n",
      "Epoch 4/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0368 - accuracy: 0.9872 - val_loss: 0.0221 - val_accuracy: 0.9907\n",
      "Epoch 5/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0358 - accuracy: 0.9874 - val_loss: 0.0213 - val_accuracy: 0.9917\n",
      "Epoch 6/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0353 - accuracy: 0.9878 - val_loss: 0.0313 - val_accuracy: 0.9869\n",
      "Epoch 7/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0352 - accuracy: 0.9877 - val_loss: 0.0221 - val_accuracy: 0.9911\n",
      "Epoch 8/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0334 - accuracy: 0.9882 - val_loss: 0.0202 - val_accuracy: 0.9925\n",
      "Epoch 9/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0335 - accuracy: 0.9883 - val_loss: 0.0256 - val_accuracy: 0.9901\n",
      "Epoch 10/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0326 - accuracy: 0.9889 - val_loss: 0.0251 - val_accuracy: 0.9905\n",
      "Epoch 11/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0332 - accuracy: 0.9881 - val_loss: 0.0299 - val_accuracy: 0.9885\n",
      "Epoch 12/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0323 - accuracy: 0.9885 - val_loss: 0.0249 - val_accuracy: 0.9906\n",
      "Epoch 13/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0312 - accuracy: 0.9886 - val_loss: 0.0281 - val_accuracy: 0.9893\n",
      "Epoch 14/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0307 - accuracy: 0.9890 - val_loss: 0.0310 - val_accuracy: 0.9884\n",
      "Epoch 15/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0321 - accuracy: 0.9885 - val_loss: 0.0311 - val_accuracy: 0.9873\n",
      "Epoch 16/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0304 - accuracy: 0.9891 - val_loss: 0.0288 - val_accuracy: 0.9889\n",
      "Epoch 17/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0301 - accuracy: 0.9894 - val_loss: 0.0318 - val_accuracy: 0.9888\n",
      "Epoch 18/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0313 - accuracy: 0.9886 - val_loss: 0.0324 - val_accuracy: 0.9879\n",
      "Epoch 19/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0303 - accuracy: 0.9891 - val_loss: 0.0337 - val_accuracy: 0.9875\n",
      "Epoch 20/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0297 - accuracy: 0.9893 - val_loss: 0.0348 - val_accuracy: 0.9872\n",
      "Epoch 21/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0296 - accuracy: 0.9893 - val_loss: 0.0353 - val_accuracy: 0.9868\n",
      "Epoch 22/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0300 - accuracy: 0.9891 - val_loss: 0.0327 - val_accuracy: 0.9883\n",
      "Epoch 23/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0288 - accuracy: 0.9900 - val_loss: 0.0399 - val_accuracy: 0.9846\n",
      "Epoch 24/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0296 - accuracy: 0.9893 - val_loss: 0.0386 - val_accuracy: 0.9858\n",
      "Epoch 25/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0296 - accuracy: 0.9891 - val_loss: 0.0416 - val_accuracy: 0.9847\n",
      "Epoch 26/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0296 - accuracy: 0.9891 - val_loss: 0.0412 - val_accuracy: 0.9848\n",
      "Epoch 27/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0288 - accuracy: 0.9893 - val_loss: 0.0395 - val_accuracy: 0.9855\n",
      "Epoch 28/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0286 - accuracy: 0.9896 - val_loss: 0.0392 - val_accuracy: 0.9866\n",
      "Epoch 29/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0277 - accuracy: 0.9900 - val_loss: 0.0416 - val_accuracy: 0.9855\n",
      "Epoch 30/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0276 - accuracy: 0.9902 - val_loss: 0.0429 - val_accuracy: 0.9848\n",
      "Epoch 31/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0275 - accuracy: 0.9899 - val_loss: 0.0443 - val_accuracy: 0.9855\n",
      "Epoch 32/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0272 - accuracy: 0.9904 - val_loss: 0.0423 - val_accuracy: 0.9849\n",
      "Epoch 33/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0277 - accuracy: 0.9901 - val_loss: 0.0452 - val_accuracy: 0.9844\n",
      "Epoch 34/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0272 - accuracy: 0.9901 - val_loss: 0.0432 - val_accuracy: 0.9853\n",
      "Epoch 35/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0263 - accuracy: 0.9906 - val_loss: 0.0468 - val_accuracy: 0.9840\n",
      "Epoch 36/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0271 - accuracy: 0.9903 - val_loss: 0.0455 - val_accuracy: 0.9853\n",
      "Epoch 37/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0272 - accuracy: 0.9899 - val_loss: 0.0466 - val_accuracy: 0.9851\n",
      "Epoch 38/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0264 - accuracy: 0.9903 - val_loss: 0.0480 - val_accuracy: 0.9842\n",
      "Epoch 39/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0260 - accuracy: 0.9904 - val_loss: 0.0478 - val_accuracy: 0.9840\n",
      "Epoch 40/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0258 - accuracy: 0.9907 - val_loss: 0.0511 - val_accuracy: 0.9856\n",
      "Epoch 41/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0266 - accuracy: 0.9906 - val_loss: 0.0524 - val_accuracy: 0.9835\n",
      "Epoch 42/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0253 - accuracy: 0.9909 - val_loss: 0.0508 - val_accuracy: 0.9829\n",
      "Epoch 43/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0258 - accuracy: 0.9907 - val_loss: 0.0513 - val_accuracy: 0.9838\n",
      "Epoch 44/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0260 - accuracy: 0.9907 - val_loss: 0.0530 - val_accuracy: 0.9824\n",
      "Epoch 45/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0252 - accuracy: 0.9909 - val_loss: 0.0546 - val_accuracy: 0.9812\n",
      "Epoch 46/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0247 - accuracy: 0.9908 - val_loss: 0.0536 - val_accuracy: 0.9825\n",
      "Epoch 47/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0252 - accuracy: 0.9909 - val_loss: 0.0524 - val_accuracy: 0.9833\n",
      "Epoch 48/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0248 - accuracy: 0.9908 - val_loss: 0.0498 - val_accuracy: 0.9840\n",
      "Epoch 49/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0250 - accuracy: 0.9909 - val_loss: 0.0542 - val_accuracy: 0.9834\n",
      "Epoch 50/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0247 - accuracy: 0.9910 - val_loss: 0.0580 - val_accuracy: 0.9810\n",
      "Epoch 51/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0248 - accuracy: 0.9910 - val_loss: 0.0555 - val_accuracy: 0.9837\n",
      "Epoch 52/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0244 - accuracy: 0.9910 - val_loss: 0.0540 - val_accuracy: 0.9838\n",
      "Epoch 53/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0244 - accuracy: 0.9913 - val_loss: 0.0578 - val_accuracy: 0.9818\n",
      "Epoch 54/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0256 - accuracy: 0.9905 - val_loss: 0.0584 - val_accuracy: 0.9827\n",
      "Epoch 55/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0237 - accuracy: 0.9914 - val_loss: 0.0573 - val_accuracy: 0.9830\n",
      "Epoch 56/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0243 - accuracy: 0.9911 - val_loss: 0.0633 - val_accuracy: 0.9804\n",
      "Epoch 57/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0239 - accuracy: 0.9914 - val_loss: 0.0605 - val_accuracy: 0.9816\n",
      "Epoch 58/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0235 - accuracy: 0.9915 - val_loss: 0.0598 - val_accuracy: 0.9822\n",
      "Epoch 59/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0239 - accuracy: 0.9912 - val_loss: 0.0632 - val_accuracy: 0.9818\n",
      "Epoch 60/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0230 - accuracy: 0.9915 - val_loss: 0.0669 - val_accuracy: 0.9788\n",
      "622/622 - 2s - loss: 0.0669 - accuracy: 0.9788 - 2s/epoch - 3ms/step\n",
      "622/622 [==============================] - 2s 3ms/step\n",
      "Completed fold 3/10\n",
      "Epoch 1/60\n",
      "1400/1400 [==============================] - 28s 19ms/step - loss: 0.0315 - accuracy: 0.9893 - val_loss: 0.0061 - val_accuracy: 0.9985\n",
      "Epoch 2/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0301 - accuracy: 0.9895 - val_loss: 0.0063 - val_accuracy: 0.9977\n",
      "Epoch 3/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0295 - accuracy: 0.9898 - val_loss: 0.0101 - val_accuracy: 0.9961\n",
      "Epoch 4/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0295 - accuracy: 0.9895 - val_loss: 0.0113 - val_accuracy: 0.9963\n",
      "Epoch 5/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0277 - accuracy: 0.9900 - val_loss: 0.0085 - val_accuracy: 0.9970\n",
      "Epoch 6/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0286 - accuracy: 0.9900 - val_loss: 0.0095 - val_accuracy: 0.9959\n",
      "Epoch 7/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0263 - accuracy: 0.9906 - val_loss: 0.0121 - val_accuracy: 0.9956\n",
      "Epoch 8/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0259 - accuracy: 0.9907 - val_loss: 0.0121 - val_accuracy: 0.9954\n",
      "Epoch 9/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0269 - accuracy: 0.9902 - val_loss: 0.0125 - val_accuracy: 0.9953\n",
      "Epoch 10/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0265 - accuracy: 0.9905 - val_loss: 0.0121 - val_accuracy: 0.9954\n",
      "Epoch 11/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0262 - accuracy: 0.9903 - val_loss: 0.0142 - val_accuracy: 0.9948\n",
      "Epoch 12/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0265 - accuracy: 0.9904 - val_loss: 0.0158 - val_accuracy: 0.9936\n",
      "Epoch 13/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0260 - accuracy: 0.9905 - val_loss: 0.0138 - val_accuracy: 0.9947\n",
      "Epoch 14/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0249 - accuracy: 0.9908 - val_loss: 0.0152 - val_accuracy: 0.9945\n",
      "Epoch 15/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0261 - accuracy: 0.9907 - val_loss: 0.0131 - val_accuracy: 0.9952\n",
      "Epoch 16/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0247 - accuracy: 0.9908 - val_loss: 0.0159 - val_accuracy: 0.9939\n",
      "Epoch 17/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0244 - accuracy: 0.9912 - val_loss: 0.0187 - val_accuracy: 0.9926\n",
      "Epoch 18/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0239 - accuracy: 0.9910 - val_loss: 0.0170 - val_accuracy: 0.9929\n",
      "Epoch 19/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0259 - accuracy: 0.9906 - val_loss: 0.0161 - val_accuracy: 0.9930\n",
      "Epoch 20/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0249 - accuracy: 0.9907 - val_loss: 0.0170 - val_accuracy: 0.9930\n",
      "Epoch 21/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0240 - accuracy: 0.9913 - val_loss: 0.0160 - val_accuracy: 0.9936\n",
      "Epoch 22/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0237 - accuracy: 0.9914 - val_loss: 0.0204 - val_accuracy: 0.9919\n",
      "Epoch 23/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0237 - accuracy: 0.9913 - val_loss: 0.0208 - val_accuracy: 0.9920\n",
      "Epoch 24/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0239 - accuracy: 0.9912 - val_loss: 0.0175 - val_accuracy: 0.9928\n",
      "Epoch 25/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0228 - accuracy: 0.9917 - val_loss: 0.0184 - val_accuracy: 0.9929\n",
      "Epoch 26/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0236 - accuracy: 0.9915 - val_loss: 0.0196 - val_accuracy: 0.9922\n",
      "Epoch 27/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0242 - accuracy: 0.9911 - val_loss: 0.0209 - val_accuracy: 0.9910\n",
      "Epoch 28/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0230 - accuracy: 0.9914 - val_loss: 0.0201 - val_accuracy: 0.9922\n",
      "Epoch 29/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0231 - accuracy: 0.9914 - val_loss: 0.0205 - val_accuracy: 0.9923\n",
      "Epoch 30/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0224 - accuracy: 0.9919 - val_loss: 0.0217 - val_accuracy: 0.9915\n",
      "Epoch 31/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0229 - accuracy: 0.9917 - val_loss: 0.0241 - val_accuracy: 0.9910\n",
      "Epoch 32/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0223 - accuracy: 0.9915 - val_loss: 0.0225 - val_accuracy: 0.9910\n",
      "Epoch 33/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0225 - accuracy: 0.9917 - val_loss: 0.0251 - val_accuracy: 0.9897\n",
      "Epoch 34/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0229 - accuracy: 0.9916 - val_loss: 0.0247 - val_accuracy: 0.9901\n",
      "Epoch 35/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0230 - accuracy: 0.9914 - val_loss: 0.0242 - val_accuracy: 0.9903\n",
      "Epoch 36/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0215 - accuracy: 0.9919 - val_loss: 0.0242 - val_accuracy: 0.9908\n",
      "Epoch 37/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0217 - accuracy: 0.9921 - val_loss: 0.0271 - val_accuracy: 0.9896\n",
      "Epoch 38/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0222 - accuracy: 0.9920 - val_loss: 0.0285 - val_accuracy: 0.9902\n",
      "Epoch 39/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0219 - accuracy: 0.9921 - val_loss: 0.0291 - val_accuracy: 0.9885\n",
      "Epoch 40/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0217 - accuracy: 0.9920 - val_loss: 0.0296 - val_accuracy: 0.9891\n",
      "Epoch 41/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0221 - accuracy: 0.9919 - val_loss: 0.0278 - val_accuracy: 0.9895\n",
      "Epoch 42/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0213 - accuracy: 0.9924 - val_loss: 0.0355 - val_accuracy: 0.9865\n",
      "Epoch 43/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0214 - accuracy: 0.9921 - val_loss: 0.0304 - val_accuracy: 0.9889\n",
      "Epoch 44/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0213 - accuracy: 0.9922 - val_loss: 0.0337 - val_accuracy: 0.9870\n",
      "Epoch 45/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0208 - accuracy: 0.9922 - val_loss: 0.0287 - val_accuracy: 0.9890\n",
      "Epoch 46/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0223 - accuracy: 0.9919 - val_loss: 0.0304 - val_accuracy: 0.9884\n",
      "Epoch 47/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0208 - accuracy: 0.9923 - val_loss: 0.0310 - val_accuracy: 0.9882\n",
      "Epoch 48/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0211 - accuracy: 0.9920 - val_loss: 0.0342 - val_accuracy: 0.9872\n",
      "Epoch 49/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0221 - accuracy: 0.9919 - val_loss: 0.0307 - val_accuracy: 0.9889\n",
      "Epoch 50/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0209 - accuracy: 0.9923 - val_loss: 0.0338 - val_accuracy: 0.9866\n",
      "Epoch 51/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0203 - accuracy: 0.9924 - val_loss: 0.0367 - val_accuracy: 0.9870\n",
      "Epoch 52/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0211 - accuracy: 0.9924 - val_loss: 0.0356 - val_accuracy: 0.9872\n",
      "Epoch 53/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0212 - accuracy: 0.9923 - val_loss: 0.0327 - val_accuracy: 0.9879\n",
      "Epoch 54/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0208 - accuracy: 0.9923 - val_loss: 0.0369 - val_accuracy: 0.9862\n",
      "Epoch 55/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0203 - accuracy: 0.9927 - val_loss: 0.0394 - val_accuracy: 0.9864\n",
      "Epoch 56/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0202 - accuracy: 0.9925 - val_loss: 0.0360 - val_accuracy: 0.9870\n",
      "Epoch 57/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0201 - accuracy: 0.9925 - val_loss: 0.0369 - val_accuracy: 0.9872\n",
      "Epoch 58/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0207 - accuracy: 0.9923 - val_loss: 0.0366 - val_accuracy: 0.9875\n",
      "Epoch 59/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0211 - accuracy: 0.9922 - val_loss: 0.0362 - val_accuracy: 0.9872\n",
      "Epoch 60/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0212 - accuracy: 0.9921 - val_loss: 0.0367 - val_accuracy: 0.9860\n",
      "622/622 - 2s - loss: 0.0367 - accuracy: 0.9860 - 2s/epoch - 3ms/step\n",
      "622/622 [==============================] - 2s 3ms/step\n",
      "Completed fold 4/10\n",
      "Epoch 1/60\n",
      "1400/1400 [==============================] - 28s 18ms/step - loss: 0.0264 - accuracy: 0.9907 - val_loss: 0.0045 - val_accuracy: 0.9986\n",
      "Epoch 2/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0255 - accuracy: 0.9911 - val_loss: 0.0052 - val_accuracy: 0.9985\n",
      "Epoch 3/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0248 - accuracy: 0.9910 - val_loss: 0.0056 - val_accuracy: 0.9984\n",
      "Epoch 4/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0236 - accuracy: 0.9915 - val_loss: 0.0069 - val_accuracy: 0.9973\n",
      "Epoch 5/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0233 - accuracy: 0.9915 - val_loss: 0.0089 - val_accuracy: 0.9966\n",
      "Epoch 6/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0229 - accuracy: 0.9917 - val_loss: 0.0053 - val_accuracy: 0.9985\n",
      "Epoch 7/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0224 - accuracy: 0.9919 - val_loss: 0.0062 - val_accuracy: 0.9979\n",
      "Epoch 8/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0218 - accuracy: 0.9919 - val_loss: 0.0090 - val_accuracy: 0.9970\n",
      "Epoch 9/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0223 - accuracy: 0.9918 - val_loss: 0.0096 - val_accuracy: 0.9969\n",
      "Epoch 10/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0221 - accuracy: 0.9921 - val_loss: 0.0074 - val_accuracy: 0.9972\n",
      "Epoch 11/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0215 - accuracy: 0.9921 - val_loss: 0.0094 - val_accuracy: 0.9967\n",
      "Epoch 12/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0227 - accuracy: 0.9918 - val_loss: 0.0079 - val_accuracy: 0.9971\n",
      "Epoch 13/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0216 - accuracy: 0.9919 - val_loss: 0.0099 - val_accuracy: 0.9959\n",
      "Epoch 14/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0206 - accuracy: 0.9923 - val_loss: 0.0092 - val_accuracy: 0.9962\n",
      "Epoch 15/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0213 - accuracy: 0.9922 - val_loss: 0.0112 - val_accuracy: 0.9954\n",
      "Epoch 16/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0209 - accuracy: 0.9921 - val_loss: 0.0141 - val_accuracy: 0.9950\n",
      "Epoch 17/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0214 - accuracy: 0.9920 - val_loss: 0.0111 - val_accuracy: 0.9963\n",
      "Epoch 18/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0214 - accuracy: 0.9921 - val_loss: 0.0138 - val_accuracy: 0.9947\n",
      "Epoch 19/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0205 - accuracy: 0.9925 - val_loss: 0.0145 - val_accuracy: 0.9946\n",
      "Epoch 20/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0208 - accuracy: 0.9924 - val_loss: 0.0153 - val_accuracy: 0.9938\n",
      "Epoch 21/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0211 - accuracy: 0.9922 - val_loss: 0.0127 - val_accuracy: 0.9953\n",
      "Epoch 22/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0200 - accuracy: 0.9925 - val_loss: 0.0133 - val_accuracy: 0.9950\n",
      "Epoch 23/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0205 - accuracy: 0.9927 - val_loss: 0.0140 - val_accuracy: 0.9946\n",
      "Epoch 24/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0207 - accuracy: 0.9923 - val_loss: 0.0138 - val_accuracy: 0.9943\n",
      "Epoch 25/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0196 - accuracy: 0.9928 - val_loss: 0.0165 - val_accuracy: 0.9942\n",
      "Epoch 26/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0195 - accuracy: 0.9928 - val_loss: 0.0155 - val_accuracy: 0.9937\n",
      "Epoch 27/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0202 - accuracy: 0.9926 - val_loss: 0.0153 - val_accuracy: 0.9939\n",
      "Epoch 28/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0206 - accuracy: 0.9925 - val_loss: 0.0154 - val_accuracy: 0.9938\n",
      "Epoch 29/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0196 - accuracy: 0.9926 - val_loss: 0.0193 - val_accuracy: 0.9928\n",
      "Epoch 30/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0192 - accuracy: 0.9927 - val_loss: 0.0179 - val_accuracy: 0.9931\n",
      "Epoch 31/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0192 - accuracy: 0.9930 - val_loss: 0.0186 - val_accuracy: 0.9926\n",
      "Epoch 32/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0194 - accuracy: 0.9928 - val_loss: 0.0176 - val_accuracy: 0.9931\n",
      "Epoch 33/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0191 - accuracy: 0.9928 - val_loss: 0.0173 - val_accuracy: 0.9928\n",
      "Epoch 34/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0194 - accuracy: 0.9930 - val_loss: 0.0200 - val_accuracy: 0.9916\n",
      "Epoch 35/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0196 - accuracy: 0.9927 - val_loss: 0.0193 - val_accuracy: 0.9927\n",
      "Epoch 36/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0191 - accuracy: 0.9928 - val_loss: 0.0180 - val_accuracy: 0.9937\n",
      "Epoch 37/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0200 - accuracy: 0.9925 - val_loss: 0.0215 - val_accuracy: 0.9920\n",
      "Epoch 38/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0194 - accuracy: 0.9927 - val_loss: 0.0209 - val_accuracy: 0.9927\n",
      "Epoch 39/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0194 - accuracy: 0.9929 - val_loss: 0.0217 - val_accuracy: 0.9925\n",
      "Epoch 40/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0190 - accuracy: 0.9930 - val_loss: 0.0212 - val_accuracy: 0.9919\n",
      "Epoch 41/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0189 - accuracy: 0.9929 - val_loss: 0.0194 - val_accuracy: 0.9938\n",
      "Epoch 42/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0190 - accuracy: 0.9931 - val_loss: 0.0223 - val_accuracy: 0.9917\n",
      "Epoch 43/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0185 - accuracy: 0.9929 - val_loss: 0.0199 - val_accuracy: 0.9924\n",
      "Epoch 44/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0186 - accuracy: 0.9928 - val_loss: 0.0211 - val_accuracy: 0.9926\n",
      "Epoch 45/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0185 - accuracy: 0.9934 - val_loss: 0.0240 - val_accuracy: 0.9913\n",
      "Epoch 46/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0190 - accuracy: 0.9929 - val_loss: 0.0233 - val_accuracy: 0.9923\n",
      "Epoch 47/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0189 - accuracy: 0.9931 - val_loss: 0.0246 - val_accuracy: 0.9910\n",
      "Epoch 48/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0183 - accuracy: 0.9935 - val_loss: 0.0245 - val_accuracy: 0.9907\n",
      "Epoch 49/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0191 - accuracy: 0.9929 - val_loss: 0.0293 - val_accuracy: 0.9892\n",
      "Epoch 50/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0186 - accuracy: 0.9929 - val_loss: 0.0280 - val_accuracy: 0.9893\n",
      "Epoch 51/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0191 - accuracy: 0.9930 - val_loss: 0.0244 - val_accuracy: 0.9913\n",
      "Epoch 52/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0188 - accuracy: 0.9930 - val_loss: 0.0276 - val_accuracy: 0.9898\n",
      "Epoch 53/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0182 - accuracy: 0.9930 - val_loss: 0.0274 - val_accuracy: 0.9910\n",
      "Epoch 54/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0181 - accuracy: 0.9934 - val_loss: 0.0283 - val_accuracy: 0.9889\n",
      "Epoch 55/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0184 - accuracy: 0.9932 - val_loss: 0.0289 - val_accuracy: 0.9896\n",
      "Epoch 56/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0175 - accuracy: 0.9935 - val_loss: 0.0256 - val_accuracy: 0.9909\n",
      "Epoch 57/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0191 - accuracy: 0.9930 - val_loss: 0.0286 - val_accuracy: 0.9902\n",
      "Epoch 58/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0177 - accuracy: 0.9934 - val_loss: 0.0285 - val_accuracy: 0.9905\n",
      "Epoch 59/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0178 - accuracy: 0.9931 - val_loss: 0.0290 - val_accuracy: 0.9901\n",
      "Epoch 60/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0183 - accuracy: 0.9931 - val_loss: 0.0294 - val_accuracy: 0.9901\n",
      "622/622 - 2s - loss: 0.0294 - accuracy: 0.9901 - 2s/epoch - 3ms/step\n",
      "622/622 [==============================] - 2s 3ms/step\n",
      "Completed fold 5/10\n",
      "Epoch 1/60\n",
      "1400/1400 [==============================] - 28s 18ms/step - loss: 0.0230 - accuracy: 0.9919 - val_loss: 0.0048 - val_accuracy: 0.9984\n",
      "Epoch 2/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0227 - accuracy: 0.9921 - val_loss: 0.0066 - val_accuracy: 0.9979\n",
      "Epoch 3/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0217 - accuracy: 0.9921 - val_loss: 0.0042 - val_accuracy: 0.9986\n",
      "Epoch 4/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0199 - accuracy: 0.9929 - val_loss: 0.0052 - val_accuracy: 0.9979\n",
      "Epoch 5/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0204 - accuracy: 0.9923 - val_loss: 0.0064 - val_accuracy: 0.9974\n",
      "Epoch 6/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0203 - accuracy: 0.9927 - val_loss: 0.0057 - val_accuracy: 0.9979\n",
      "Epoch 7/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0199 - accuracy: 0.9926 - val_loss: 0.0074 - val_accuracy: 0.9975\n",
      "Epoch 8/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0191 - accuracy: 0.9931 - val_loss: 0.0072 - val_accuracy: 0.9975\n",
      "Epoch 9/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0203 - accuracy: 0.9925 - val_loss: 0.0062 - val_accuracy: 0.9978\n",
      "Epoch 10/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0189 - accuracy: 0.9929 - val_loss: 0.0068 - val_accuracy: 0.9971\n",
      "Epoch 11/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0199 - accuracy: 0.9927 - val_loss: 0.0082 - val_accuracy: 0.9966\n",
      "Epoch 12/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0192 - accuracy: 0.9928 - val_loss: 0.0086 - val_accuracy: 0.9967\n",
      "Epoch 13/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0193 - accuracy: 0.9930 - val_loss: 0.0095 - val_accuracy: 0.9968\n",
      "Epoch 14/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0189 - accuracy: 0.9930 - val_loss: 0.0086 - val_accuracy: 0.9967\n",
      "Epoch 15/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0179 - accuracy: 0.9934 - val_loss: 0.0112 - val_accuracy: 0.9957\n",
      "Epoch 16/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0186 - accuracy: 0.9930 - val_loss: 0.0091 - val_accuracy: 0.9966\n",
      "Epoch 17/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0191 - accuracy: 0.9929 - val_loss: 0.0092 - val_accuracy: 0.9963\n",
      "Epoch 18/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0180 - accuracy: 0.9934 - val_loss: 0.0096 - val_accuracy: 0.9963\n",
      "Epoch 19/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0192 - accuracy: 0.9930 - val_loss: 0.0091 - val_accuracy: 0.9966\n",
      "Epoch 20/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0182 - accuracy: 0.9933 - val_loss: 0.0109 - val_accuracy: 0.9962\n",
      "Epoch 21/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0183 - accuracy: 0.9934 - val_loss: 0.0144 - val_accuracy: 0.9949\n",
      "Epoch 22/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0182 - accuracy: 0.9934 - val_loss: 0.0097 - val_accuracy: 0.9961\n",
      "Epoch 23/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0180 - accuracy: 0.9933 - val_loss: 0.0123 - val_accuracy: 0.9956\n",
      "Epoch 24/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0179 - accuracy: 0.9934 - val_loss: 0.0130 - val_accuracy: 0.9950\n",
      "Epoch 25/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0180 - accuracy: 0.9933 - val_loss: 0.0129 - val_accuracy: 0.9953\n",
      "Epoch 26/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0181 - accuracy: 0.9932 - val_loss: 0.0154 - val_accuracy: 0.9936\n",
      "Epoch 27/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0180 - accuracy: 0.9935 - val_loss: 0.0106 - val_accuracy: 0.9958\n",
      "Epoch 28/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0178 - accuracy: 0.9933 - val_loss: 0.0126 - val_accuracy: 0.9950\n",
      "Epoch 29/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0178 - accuracy: 0.9933 - val_loss: 0.0127 - val_accuracy: 0.9954\n",
      "Epoch 30/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0178 - accuracy: 0.9932 - val_loss: 0.0133 - val_accuracy: 0.9953\n",
      "Epoch 31/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0180 - accuracy: 0.9933 - val_loss: 0.0123 - val_accuracy: 0.9956\n",
      "Epoch 32/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0179 - accuracy: 0.9935 - val_loss: 0.0120 - val_accuracy: 0.9956\n",
      "Epoch 33/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0179 - accuracy: 0.9934 - val_loss: 0.0126 - val_accuracy: 0.9948\n",
      "Epoch 34/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0168 - accuracy: 0.9938 - val_loss: 0.0139 - val_accuracy: 0.9943\n",
      "Epoch 35/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0174 - accuracy: 0.9936 - val_loss: 0.0137 - val_accuracy: 0.9949\n",
      "Epoch 36/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0173 - accuracy: 0.9937 - val_loss: 0.0136 - val_accuracy: 0.9953\n",
      "Epoch 37/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0174 - accuracy: 0.9934 - val_loss: 0.0170 - val_accuracy: 0.9936\n",
      "Epoch 38/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0174 - accuracy: 0.9936 - val_loss: 0.0148 - val_accuracy: 0.9945\n",
      "Epoch 39/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0173 - accuracy: 0.9938 - val_loss: 0.0152 - val_accuracy: 0.9950\n",
      "Epoch 40/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0174 - accuracy: 0.9936 - val_loss: 0.0164 - val_accuracy: 0.9937\n",
      "Epoch 41/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0171 - accuracy: 0.9937 - val_loss: 0.0185 - val_accuracy: 0.9931\n",
      "Epoch 42/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0171 - accuracy: 0.9937 - val_loss: 0.0182 - val_accuracy: 0.9932\n",
      "Epoch 43/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0172 - accuracy: 0.9936 - val_loss: 0.0166 - val_accuracy: 0.9936\n",
      "Epoch 44/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0169 - accuracy: 0.9935 - val_loss: 0.0189 - val_accuracy: 0.9934\n",
      "Epoch 45/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0163 - accuracy: 0.9936 - val_loss: 0.0189 - val_accuracy: 0.9926\n",
      "Epoch 46/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0175 - accuracy: 0.9934 - val_loss: 0.0193 - val_accuracy: 0.9927\n",
      "Epoch 47/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0177 - accuracy: 0.9935 - val_loss: 0.0214 - val_accuracy: 0.9920\n",
      "Epoch 48/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0170 - accuracy: 0.9938 - val_loss: 0.0180 - val_accuracy: 0.9931\n",
      "Epoch 49/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0169 - accuracy: 0.9936 - val_loss: 0.0190 - val_accuracy: 0.9931\n",
      "Epoch 50/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0170 - accuracy: 0.9938 - val_loss: 0.0214 - val_accuracy: 0.9925\n",
      "Epoch 51/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0164 - accuracy: 0.9939 - val_loss: 0.0199 - val_accuracy: 0.9922\n",
      "Epoch 52/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0170 - accuracy: 0.9939 - val_loss: 0.0227 - val_accuracy: 0.9915\n",
      "Epoch 53/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0167 - accuracy: 0.9936 - val_loss: 0.0195 - val_accuracy: 0.9930\n",
      "Epoch 54/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0167 - accuracy: 0.9939 - val_loss: 0.0202 - val_accuracy: 0.9929\n",
      "Epoch 55/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0170 - accuracy: 0.9938 - val_loss: 0.0225 - val_accuracy: 0.9919\n",
      "Epoch 56/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0164 - accuracy: 0.9937 - val_loss: 0.0236 - val_accuracy: 0.9917\n",
      "Epoch 57/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0170 - accuracy: 0.9938 - val_loss: 0.0240 - val_accuracy: 0.9910\n",
      "Epoch 58/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0164 - accuracy: 0.9938 - val_loss: 0.0259 - val_accuracy: 0.9905\n",
      "Epoch 59/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0165 - accuracy: 0.9939 - val_loss: 0.0216 - val_accuracy: 0.9926\n",
      "Epoch 60/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0167 - accuracy: 0.9939 - val_loss: 0.0248 - val_accuracy: 0.9918\n",
      "622/622 - 2s - loss: 0.0248 - accuracy: 0.9918 - 2s/epoch - 3ms/step\n",
      "622/622 [==============================] - 2s 3ms/step\n",
      "Completed fold 6/10\n",
      "Epoch 1/60\n",
      "1400/1400 [==============================] - 29s 19ms/step - loss: 0.0211 - accuracy: 0.9925 - val_loss: 0.0040 - val_accuracy: 0.9985\n",
      "Epoch 2/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0203 - accuracy: 0.9926 - val_loss: 0.0053 - val_accuracy: 0.9979\n",
      "Epoch 3/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0198 - accuracy: 0.9929 - val_loss: 0.0041 - val_accuracy: 0.9986\n",
      "Epoch 4/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0184 - accuracy: 0.9933 - val_loss: 0.0060 - val_accuracy: 0.9979\n",
      "Epoch 5/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0176 - accuracy: 0.9937 - val_loss: 0.0040 - val_accuracy: 0.9986\n",
      "Epoch 6/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0183 - accuracy: 0.9932 - val_loss: 0.0055 - val_accuracy: 0.9981\n",
      "Epoch 7/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0181 - accuracy: 0.9935 - val_loss: 0.0058 - val_accuracy: 0.9979\n",
      "Epoch 8/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0177 - accuracy: 0.9935 - val_loss: 0.0069 - val_accuracy: 0.9974\n",
      "Epoch 9/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0180 - accuracy: 0.9932 - val_loss: 0.0061 - val_accuracy: 0.9977\n",
      "Epoch 10/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0190 - accuracy: 0.9932 - val_loss: 0.0059 - val_accuracy: 0.9975\n",
      "Epoch 11/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0173 - accuracy: 0.9936 - val_loss: 0.0060 - val_accuracy: 0.9975\n",
      "Epoch 12/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0169 - accuracy: 0.9939 - val_loss: 0.0055 - val_accuracy: 0.9979\n",
      "Epoch 13/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0176 - accuracy: 0.9934 - val_loss: 0.0064 - val_accuracy: 0.9976\n",
      "Epoch 14/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0175 - accuracy: 0.9935 - val_loss: 0.0079 - val_accuracy: 0.9968\n",
      "Epoch 15/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0179 - accuracy: 0.9935 - val_loss: 0.0074 - val_accuracy: 0.9966\n",
      "Epoch 16/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0169 - accuracy: 0.9938 - val_loss: 0.0083 - val_accuracy: 0.9966\n",
      "Epoch 17/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0172 - accuracy: 0.9937 - val_loss: 0.0085 - val_accuracy: 0.9965\n",
      "Epoch 18/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0175 - accuracy: 0.9936 - val_loss: 0.0099 - val_accuracy: 0.9960\n",
      "Epoch 19/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0170 - accuracy: 0.9936 - val_loss: 0.0087 - val_accuracy: 0.9966\n",
      "Epoch 20/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0171 - accuracy: 0.9937 - val_loss: 0.0092 - val_accuracy: 0.9960\n",
      "Epoch 21/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0166 - accuracy: 0.9938 - val_loss: 0.0102 - val_accuracy: 0.9957\n",
      "Epoch 22/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0166 - accuracy: 0.9937 - val_loss: 0.0069 - val_accuracy: 0.9973\n",
      "Epoch 23/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0176 - accuracy: 0.9937 - val_loss: 0.0108 - val_accuracy: 0.9955\n",
      "Epoch 24/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0160 - accuracy: 0.9942 - val_loss: 0.0088 - val_accuracy: 0.9967\n",
      "Epoch 25/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0163 - accuracy: 0.9939 - val_loss: 0.0101 - val_accuracy: 0.9955\n",
      "Epoch 26/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0168 - accuracy: 0.9939 - val_loss: 0.0110 - val_accuracy: 0.9956\n",
      "Epoch 27/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0165 - accuracy: 0.9937 - val_loss: 0.0082 - val_accuracy: 0.9966\n",
      "Epoch 28/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0164 - accuracy: 0.9939 - val_loss: 0.0104 - val_accuracy: 0.9956\n",
      "Epoch 29/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0165 - accuracy: 0.9938 - val_loss: 0.0108 - val_accuracy: 0.9955\n",
      "Epoch 30/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0162 - accuracy: 0.9941 - val_loss: 0.0139 - val_accuracy: 0.9942\n",
      "Epoch 31/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0154 - accuracy: 0.9943 - val_loss: 0.0114 - val_accuracy: 0.9950\n",
      "Epoch 32/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0155 - accuracy: 0.9942 - val_loss: 0.0133 - val_accuracy: 0.9947\n",
      "Epoch 33/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0161 - accuracy: 0.9941 - val_loss: 0.0129 - val_accuracy: 0.9954\n",
      "Epoch 34/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0170 - accuracy: 0.9938 - val_loss: 0.0128 - val_accuracy: 0.9948\n",
      "Epoch 35/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0161 - accuracy: 0.9940 - val_loss: 0.0117 - val_accuracy: 0.9952\n",
      "Epoch 36/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0160 - accuracy: 0.9943 - val_loss: 0.0154 - val_accuracy: 0.9937\n",
      "Epoch 37/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0159 - accuracy: 0.9939 - val_loss: 0.0138 - val_accuracy: 0.9949\n",
      "Epoch 38/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0162 - accuracy: 0.9940 - val_loss: 0.0141 - val_accuracy: 0.9948\n",
      "Epoch 39/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0159 - accuracy: 0.9942 - val_loss: 0.0142 - val_accuracy: 0.9947\n",
      "Epoch 40/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0159 - accuracy: 0.9941 - val_loss: 0.0188 - val_accuracy: 0.9923\n",
      "Epoch 41/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0157 - accuracy: 0.9940 - val_loss: 0.0143 - val_accuracy: 0.9943\n",
      "Epoch 42/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0159 - accuracy: 0.9940 - val_loss: 0.0129 - val_accuracy: 0.9944\n",
      "Epoch 43/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0153 - accuracy: 0.9943 - val_loss: 0.0177 - val_accuracy: 0.9929\n",
      "Epoch 44/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0152 - accuracy: 0.9944 - val_loss: 0.0152 - val_accuracy: 0.9940\n",
      "Epoch 45/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0156 - accuracy: 0.9939 - val_loss: 0.0164 - val_accuracy: 0.9936\n",
      "Epoch 46/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0159 - accuracy: 0.9941 - val_loss: 0.0165 - val_accuracy: 0.9933\n",
      "Epoch 47/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0151 - accuracy: 0.9944 - val_loss: 0.0210 - val_accuracy: 0.9916\n",
      "Epoch 48/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0163 - accuracy: 0.9941 - val_loss: 0.0179 - val_accuracy: 0.9935\n",
      "Epoch 49/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0155 - accuracy: 0.9941 - val_loss: 0.0173 - val_accuracy: 0.9934\n",
      "Epoch 50/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0155 - accuracy: 0.9943 - val_loss: 0.0163 - val_accuracy: 0.9935\n",
      "Epoch 51/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0159 - accuracy: 0.9940 - val_loss: 0.0182 - val_accuracy: 0.9930\n",
      "Epoch 52/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0158 - accuracy: 0.9941 - val_loss: 0.0204 - val_accuracy: 0.9923\n",
      "Epoch 53/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0158 - accuracy: 0.9941 - val_loss: 0.0189 - val_accuracy: 0.9933\n",
      "Epoch 54/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0160 - accuracy: 0.9942 - val_loss: 0.0196 - val_accuracy: 0.9930\n",
      "Epoch 55/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0153 - accuracy: 0.9945 - val_loss: 0.0180 - val_accuracy: 0.9928\n",
      "Epoch 56/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0148 - accuracy: 0.9944 - val_loss: 0.0206 - val_accuracy: 0.9920\n",
      "Epoch 57/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0154 - accuracy: 0.9943 - val_loss: 0.0179 - val_accuracy: 0.9933\n",
      "Epoch 58/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0153 - accuracy: 0.9944 - val_loss: 0.0221 - val_accuracy: 0.9918\n",
      "Epoch 59/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0150 - accuracy: 0.9944 - val_loss: 0.0181 - val_accuracy: 0.9933\n",
      "Epoch 60/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0156 - accuracy: 0.9941 - val_loss: 0.0211 - val_accuracy: 0.9923\n",
      "622/622 - 2s - loss: 0.0211 - accuracy: 0.9923 - 2s/epoch - 3ms/step\n",
      "622/622 [==============================] - 2s 3ms/step\n",
      "Completed fold 7/10\n",
      "Epoch 1/60\n",
      "1400/1400 [==============================] - 29s 19ms/step - loss: 0.0189 - accuracy: 0.9931 - val_loss: 0.0040 - val_accuracy: 0.9987\n",
      "Epoch 2/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0192 - accuracy: 0.9932 - val_loss: 0.0040 - val_accuracy: 0.9983\n",
      "Epoch 3/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0178 - accuracy: 0.9936 - val_loss: 0.0039 - val_accuracy: 0.9985\n",
      "Epoch 4/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0170 - accuracy: 0.9939 - val_loss: 0.0039 - val_accuracy: 0.9986\n",
      "Epoch 5/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0176 - accuracy: 0.9937 - val_loss: 0.0035 - val_accuracy: 0.9986\n",
      "Epoch 6/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0170 - accuracy: 0.9937 - val_loss: 0.0049 - val_accuracy: 0.9981\n",
      "Epoch 7/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0176 - accuracy: 0.9934 - val_loss: 0.0052 - val_accuracy: 0.9981\n",
      "Epoch 8/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0171 - accuracy: 0.9935 - val_loss: 0.0042 - val_accuracy: 0.9983\n",
      "Epoch 9/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0171 - accuracy: 0.9938 - val_loss: 0.0049 - val_accuracy: 0.9982\n",
      "Epoch 10/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0163 - accuracy: 0.9940 - val_loss: 0.0049 - val_accuracy: 0.9982\n",
      "Epoch 11/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0167 - accuracy: 0.9940 - val_loss: 0.0053 - val_accuracy: 0.9981\n",
      "Epoch 12/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0169 - accuracy: 0.9938 - val_loss: 0.0052 - val_accuracy: 0.9981\n",
      "Epoch 13/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0151 - accuracy: 0.9945 - val_loss: 0.0049 - val_accuracy: 0.9980\n",
      "Epoch 14/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0160 - accuracy: 0.9941 - val_loss: 0.0071 - val_accuracy: 0.9974\n",
      "Epoch 15/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0160 - accuracy: 0.9939 - val_loss: 0.0065 - val_accuracy: 0.9976\n",
      "Epoch 16/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0162 - accuracy: 0.9941 - val_loss: 0.0050 - val_accuracy: 0.9982\n",
      "Epoch 17/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0158 - accuracy: 0.9941 - val_loss: 0.0081 - val_accuracy: 0.9971\n",
      "Epoch 18/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0159 - accuracy: 0.9940 - val_loss: 0.0063 - val_accuracy: 0.9974\n",
      "Epoch 19/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0163 - accuracy: 0.9939 - val_loss: 0.0066 - val_accuracy: 0.9976\n",
      "Epoch 20/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0159 - accuracy: 0.9943 - val_loss: 0.0070 - val_accuracy: 0.9974\n",
      "Epoch 21/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0164 - accuracy: 0.9939 - val_loss: 0.0074 - val_accuracy: 0.9969\n",
      "Epoch 22/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0156 - accuracy: 0.9942 - val_loss: 0.0074 - val_accuracy: 0.9972\n",
      "Epoch 23/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0164 - accuracy: 0.9940 - val_loss: 0.0068 - val_accuracy: 0.9972\n",
      "Epoch 24/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0155 - accuracy: 0.9941 - val_loss: 0.0086 - val_accuracy: 0.9969\n",
      "Epoch 25/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0154 - accuracy: 0.9944 - val_loss: 0.0075 - val_accuracy: 0.9974\n",
      "Epoch 26/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0160 - accuracy: 0.9942 - val_loss: 0.0158 - val_accuracy: 0.9932\n",
      "Epoch 27/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0155 - accuracy: 0.9942 - val_loss: 0.0088 - val_accuracy: 0.9967\n",
      "Epoch 28/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0151 - accuracy: 0.9945 - val_loss: 0.0107 - val_accuracy: 0.9959\n",
      "Epoch 29/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0150 - accuracy: 0.9944 - val_loss: 0.0091 - val_accuracy: 0.9965\n",
      "Epoch 30/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0152 - accuracy: 0.9943 - val_loss: 0.0094 - val_accuracy: 0.9959\n",
      "Epoch 31/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0158 - accuracy: 0.9939 - val_loss: 0.0100 - val_accuracy: 0.9963\n",
      "Epoch 32/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0153 - accuracy: 0.9945 - val_loss: 0.0091 - val_accuracy: 0.9967\n",
      "Epoch 33/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0151 - accuracy: 0.9945 - val_loss: 0.0110 - val_accuracy: 0.9957\n",
      "Epoch 34/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0155 - accuracy: 0.9943 - val_loss: 0.0101 - val_accuracy: 0.9963\n",
      "Epoch 35/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0150 - accuracy: 0.9943 - val_loss: 0.0111 - val_accuracy: 0.9957\n",
      "Epoch 36/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0156 - accuracy: 0.9942 - val_loss: 0.0096 - val_accuracy: 0.9964\n",
      "Epoch 37/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0148 - accuracy: 0.9946 - val_loss: 0.0110 - val_accuracy: 0.9951\n",
      "Epoch 38/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0152 - accuracy: 0.9944 - val_loss: 0.0109 - val_accuracy: 0.9962\n",
      "Epoch 39/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0146 - accuracy: 0.9943 - val_loss: 0.0099 - val_accuracy: 0.9962\n",
      "Epoch 40/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0159 - accuracy: 0.9941 - val_loss: 0.0105 - val_accuracy: 0.9963\n",
      "Epoch 41/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0143 - accuracy: 0.9947 - val_loss: 0.0109 - val_accuracy: 0.9960\n",
      "Epoch 42/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0151 - accuracy: 0.9944 - val_loss: 0.0121 - val_accuracy: 0.9960\n",
      "Epoch 43/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0146 - accuracy: 0.9946 - val_loss: 0.0111 - val_accuracy: 0.9958\n",
      "Epoch 44/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0154 - accuracy: 0.9940 - val_loss: 0.0128 - val_accuracy: 0.9949\n",
      "Epoch 45/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0146 - accuracy: 0.9948 - val_loss: 0.0129 - val_accuracy: 0.9949\n",
      "Epoch 46/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0162 - accuracy: 0.9940 - val_loss: 0.0125 - val_accuracy: 0.9955\n",
      "Epoch 47/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0144 - accuracy: 0.9945 - val_loss: 0.0126 - val_accuracy: 0.9955\n",
      "Epoch 48/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0144 - accuracy: 0.9945 - val_loss: 0.0139 - val_accuracy: 0.9948\n",
      "Epoch 49/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0153 - accuracy: 0.9942 - val_loss: 0.0139 - val_accuracy: 0.9946\n",
      "Epoch 50/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0145 - accuracy: 0.9947 - val_loss: 0.0136 - val_accuracy: 0.9950\n",
      "Epoch 51/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0151 - accuracy: 0.9945 - val_loss: 0.0143 - val_accuracy: 0.9946\n",
      "Epoch 52/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0139 - accuracy: 0.9949 - val_loss: 0.0132 - val_accuracy: 0.9951\n",
      "Epoch 53/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0144 - accuracy: 0.9946 - val_loss: 0.0141 - val_accuracy: 0.9946\n",
      "Epoch 54/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0141 - accuracy: 0.9947 - val_loss: 0.0168 - val_accuracy: 0.9947\n",
      "Epoch 55/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0147 - accuracy: 0.9945 - val_loss: 0.0157 - val_accuracy: 0.9945\n",
      "Epoch 56/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0153 - accuracy: 0.9944 - val_loss: 0.0166 - val_accuracy: 0.9940\n",
      "Epoch 57/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0150 - accuracy: 0.9945 - val_loss: 0.0160 - val_accuracy: 0.9936\n",
      "Epoch 58/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0143 - accuracy: 0.9947 - val_loss: 0.0151 - val_accuracy: 0.9943\n",
      "Epoch 59/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0134 - accuracy: 0.9949 - val_loss: 0.0165 - val_accuracy: 0.9937\n",
      "Epoch 60/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0136 - accuracy: 0.9949 - val_loss: 0.0165 - val_accuracy: 0.9943\n",
      "622/622 - 2s - loss: 0.0165 - accuracy: 0.9943 - 2s/epoch - 3ms/step\n",
      "622/622 [==============================] - 2s 3ms/step\n",
      "Completed fold 8/10\n",
      "Epoch 1/60\n",
      "1400/1400 [==============================] - 29s 19ms/step - loss: 0.0187 - accuracy: 0.9934 - val_loss: 0.0023 - val_accuracy: 0.9988\n",
      "Epoch 2/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0168 - accuracy: 0.9940 - val_loss: 0.0030 - val_accuracy: 0.9991\n",
      "Epoch 3/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0166 - accuracy: 0.9938 - val_loss: 0.0052 - val_accuracy: 0.9983\n",
      "Epoch 4/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0164 - accuracy: 0.9940 - val_loss: 0.0041 - val_accuracy: 0.9987\n",
      "Epoch 5/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0162 - accuracy: 0.9940 - val_loss: 0.0036 - val_accuracy: 0.9987\n",
      "Epoch 6/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0157 - accuracy: 0.9942 - val_loss: 0.0039 - val_accuracy: 0.9984\n",
      "Epoch 7/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0157 - accuracy: 0.9941 - val_loss: 0.0095 - val_accuracy: 0.9962\n",
      "Epoch 8/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0156 - accuracy: 0.9943 - val_loss: 0.0033 - val_accuracy: 0.9988\n",
      "Epoch 9/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0156 - accuracy: 0.9942 - val_loss: 0.0051 - val_accuracy: 0.9979\n",
      "Epoch 10/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0162 - accuracy: 0.9940 - val_loss: 0.0038 - val_accuracy: 0.9984\n",
      "Epoch 11/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0160 - accuracy: 0.9941 - val_loss: 0.0056 - val_accuracy: 0.9979\n",
      "Epoch 12/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0164 - accuracy: 0.9941 - val_loss: 0.0043 - val_accuracy: 0.9983\n",
      "Epoch 13/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0150 - accuracy: 0.9944 - val_loss: 0.0048 - val_accuracy: 0.9981\n",
      "Epoch 14/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0153 - accuracy: 0.9944 - val_loss: 0.0067 - val_accuracy: 0.9972\n",
      "Epoch 15/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0150 - accuracy: 0.9946 - val_loss: 0.0040 - val_accuracy: 0.9983\n",
      "Epoch 16/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0151 - accuracy: 0.9943 - val_loss: 0.0046 - val_accuracy: 0.9982\n",
      "Epoch 17/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0149 - accuracy: 0.9945 - val_loss: 0.0060 - val_accuracy: 0.9973\n",
      "Epoch 18/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0137 - accuracy: 0.9948 - val_loss: 0.0050 - val_accuracy: 0.9983\n",
      "Epoch 19/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0146 - accuracy: 0.9946 - val_loss: 0.0055 - val_accuracy: 0.9979\n",
      "Epoch 20/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0155 - accuracy: 0.9943 - val_loss: 0.0062 - val_accuracy: 0.9973\n",
      "Epoch 21/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0146 - accuracy: 0.9943 - val_loss: 0.0082 - val_accuracy: 0.9964\n",
      "Epoch 22/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0150 - accuracy: 0.9941 - val_loss: 0.0058 - val_accuracy: 0.9978\n",
      "Epoch 23/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0152 - accuracy: 0.9943 - val_loss: 0.0056 - val_accuracy: 0.9978\n",
      "Epoch 24/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0143 - accuracy: 0.9946 - val_loss: 0.0069 - val_accuracy: 0.9975\n",
      "Epoch 25/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0151 - accuracy: 0.9946 - val_loss: 0.0057 - val_accuracy: 0.9980\n",
      "Epoch 26/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0148 - accuracy: 0.9947 - val_loss: 0.0076 - val_accuracy: 0.9971\n",
      "Epoch 27/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0154 - accuracy: 0.9944 - val_loss: 0.0067 - val_accuracy: 0.9971\n",
      "Epoch 28/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0153 - accuracy: 0.9944 - val_loss: 0.0066 - val_accuracy: 0.9974\n",
      "Epoch 29/60\n",
      "1400/1400 [==============================] - 27s 19ms/step - loss: 0.0144 - accuracy: 0.9947 - val_loss: 0.0097 - val_accuracy: 0.9958\n",
      "Epoch 30/60\n",
      "1400/1400 [==============================] - 27s 19ms/step - loss: 0.0138 - accuracy: 0.9949 - val_loss: 0.0082 - val_accuracy: 0.9971\n",
      "Epoch 31/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0141 - accuracy: 0.9945 - val_loss: 0.0084 - val_accuracy: 0.9965\n",
      "Epoch 32/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0142 - accuracy: 0.9948 - val_loss: 0.0076 - val_accuracy: 0.9974\n",
      "Epoch 33/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0145 - accuracy: 0.9946 - val_loss: 0.0095 - val_accuracy: 0.9962\n",
      "Epoch 34/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0149 - accuracy: 0.9946 - val_loss: 0.0101 - val_accuracy: 0.9959\n",
      "Epoch 35/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0145 - accuracy: 0.9945 - val_loss: 0.0098 - val_accuracy: 0.9960\n",
      "Epoch 36/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0146 - accuracy: 0.9945 - val_loss: 0.0080 - val_accuracy: 0.9969\n",
      "Epoch 37/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0140 - accuracy: 0.9946 - val_loss: 0.0115 - val_accuracy: 0.9956\n",
      "Epoch 38/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0146 - accuracy: 0.9946 - val_loss: 0.0094 - val_accuracy: 0.9961\n",
      "Epoch 39/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0142 - accuracy: 0.9948 - val_loss: 0.0087 - val_accuracy: 0.9965\n",
      "Epoch 40/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0147 - accuracy: 0.9943 - val_loss: 0.0110 - val_accuracy: 0.9955\n",
      "Epoch 41/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0139 - accuracy: 0.9948 - val_loss: 0.0104 - val_accuracy: 0.9959\n",
      "Epoch 42/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0144 - accuracy: 0.9944 - val_loss: 0.0117 - val_accuracy: 0.9959\n",
      "Epoch 43/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0149 - accuracy: 0.9944 - val_loss: 0.0120 - val_accuracy: 0.9952\n",
      "Epoch 44/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0138 - accuracy: 0.9949 - val_loss: 0.0111 - val_accuracy: 0.9958\n",
      "Epoch 45/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0136 - accuracy: 0.9948 - val_loss: 0.0135 - val_accuracy: 0.9948\n",
      "Epoch 46/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0142 - accuracy: 0.9948 - val_loss: 0.0120 - val_accuracy: 0.9955\n",
      "Epoch 47/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0140 - accuracy: 0.9948 - val_loss: 0.0131 - val_accuracy: 0.9951\n",
      "Epoch 48/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0140 - accuracy: 0.9947 - val_loss: 0.0119 - val_accuracy: 0.9952\n",
      "Epoch 49/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0134 - accuracy: 0.9948 - val_loss: 0.0108 - val_accuracy: 0.9956\n",
      "Epoch 50/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0139 - accuracy: 0.9948 - val_loss: 0.0109 - val_accuracy: 0.9955\n",
      "Epoch 51/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0143 - accuracy: 0.9948 - val_loss: 0.0153 - val_accuracy: 0.9940\n",
      "Epoch 52/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0139 - accuracy: 0.9948 - val_loss: 0.0122 - val_accuracy: 0.9952\n",
      "Epoch 53/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0146 - accuracy: 0.9944 - val_loss: 0.0130 - val_accuracy: 0.9952\n",
      "Epoch 54/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0131 - accuracy: 0.9950 - val_loss: 0.0134 - val_accuracy: 0.9949\n",
      "Epoch 55/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0139 - accuracy: 0.9946 - val_loss: 0.0135 - val_accuracy: 0.9945\n",
      "Epoch 56/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0135 - accuracy: 0.9950 - val_loss: 0.0180 - val_accuracy: 0.9931\n",
      "Epoch 57/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0135 - accuracy: 0.9950 - val_loss: 0.0170 - val_accuracy: 0.9938\n",
      "Epoch 58/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0144 - accuracy: 0.9947 - val_loss: 0.0160 - val_accuracy: 0.9937\n",
      "Epoch 59/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0141 - accuracy: 0.9947 - val_loss: 0.0174 - val_accuracy: 0.9933\n",
      "Epoch 60/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0147 - accuracy: 0.9946 - val_loss: 0.0219 - val_accuracy: 0.9918\n",
      "622/622 - 2s - loss: 0.0219 - accuracy: 0.9918 - 2s/epoch - 3ms/step\n",
      "622/622 [==============================] - 2s 3ms/step\n",
      "Completed fold 9/10\n",
      "Epoch 1/60\n",
      "1400/1400 [==============================] - 29s 19ms/step - loss: 0.0178 - accuracy: 0.9937 - val_loss: 0.0024 - val_accuracy: 0.9987\n",
      "Epoch 2/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0158 - accuracy: 0.9940 - val_loss: 0.0034 - val_accuracy: 0.9988\n",
      "Epoch 3/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0164 - accuracy: 0.9941 - val_loss: 0.0032 - val_accuracy: 0.9989\n",
      "Epoch 4/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0162 - accuracy: 0.9940 - val_loss: 0.0028 - val_accuracy: 0.9987\n",
      "Epoch 5/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0153 - accuracy: 0.9944 - val_loss: 0.0028 - val_accuracy: 0.9988\n",
      "Epoch 6/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0157 - accuracy: 0.9943 - val_loss: 0.0035 - val_accuracy: 0.9987\n",
      "Epoch 7/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0147 - accuracy: 0.9943 - val_loss: 0.0036 - val_accuracy: 0.9984\n",
      "Epoch 8/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0155 - accuracy: 0.9943 - val_loss: 0.0033 - val_accuracy: 0.9988\n",
      "Epoch 9/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0157 - accuracy: 0.9942 - val_loss: 0.0033 - val_accuracy: 0.9986\n",
      "Epoch 10/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0157 - accuracy: 0.9943 - val_loss: 0.0034 - val_accuracy: 0.9987\n",
      "Epoch 11/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0147 - accuracy: 0.9945 - val_loss: 0.0041 - val_accuracy: 0.9984\n",
      "Epoch 12/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0155 - accuracy: 0.9942 - val_loss: 0.0042 - val_accuracy: 0.9985\n",
      "Epoch 13/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0153 - accuracy: 0.9943 - val_loss: 0.0046 - val_accuracy: 0.9982\n",
      "Epoch 14/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0142 - accuracy: 0.9947 - val_loss: 0.0055 - val_accuracy: 0.9981\n",
      "Epoch 15/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0147 - accuracy: 0.9946 - val_loss: 0.0040 - val_accuracy: 0.9986\n",
      "Epoch 16/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0143 - accuracy: 0.9947 - val_loss: 0.0043 - val_accuracy: 0.9985\n",
      "Epoch 17/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0152 - accuracy: 0.9942 - val_loss: 0.0057 - val_accuracy: 0.9978\n",
      "Epoch 18/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0148 - accuracy: 0.9945 - val_loss: 0.0051 - val_accuracy: 0.9983\n",
      "Epoch 19/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0150 - accuracy: 0.9944 - val_loss: 0.0070 - val_accuracy: 0.9972\n",
      "Epoch 20/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0151 - accuracy: 0.9945 - val_loss: 0.0053 - val_accuracy: 0.9980\n",
      "Epoch 21/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0142 - accuracy: 0.9947 - val_loss: 0.0051 - val_accuracy: 0.9981\n",
      "Epoch 22/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0135 - accuracy: 0.9949 - val_loss: 0.0047 - val_accuracy: 0.9980\n",
      "Epoch 23/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0143 - accuracy: 0.9948 - val_loss: 0.0043 - val_accuracy: 0.9984\n",
      "Epoch 24/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0143 - accuracy: 0.9948 - val_loss: 0.0056 - val_accuracy: 0.9977\n",
      "Epoch 25/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0137 - accuracy: 0.9949 - val_loss: 0.0057 - val_accuracy: 0.9980\n",
      "Epoch 26/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0139 - accuracy: 0.9948 - val_loss: 0.0056 - val_accuracy: 0.9979\n",
      "Epoch 27/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0142 - accuracy: 0.9947 - val_loss: 0.0055 - val_accuracy: 0.9979\n",
      "Epoch 28/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0144 - accuracy: 0.9947 - val_loss: 0.0060 - val_accuracy: 0.9977\n",
      "Epoch 29/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0143 - accuracy: 0.9947 - val_loss: 0.0071 - val_accuracy: 0.9970\n",
      "Epoch 30/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0137 - accuracy: 0.9950 - val_loss: 0.0052 - val_accuracy: 0.9982\n",
      "Epoch 31/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0150 - accuracy: 0.9945 - val_loss: 0.0094 - val_accuracy: 0.9963\n",
      "Epoch 32/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0133 - accuracy: 0.9950 - val_loss: 0.0061 - val_accuracy: 0.9975\n",
      "Epoch 33/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0138 - accuracy: 0.9950 - val_loss: 0.0062 - val_accuracy: 0.9976\n",
      "Epoch 34/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0141 - accuracy: 0.9949 - val_loss: 0.0093 - val_accuracy: 0.9958\n",
      "Epoch 35/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0133 - accuracy: 0.9949 - val_loss: 0.0069 - val_accuracy: 0.9971\n",
      "Epoch 36/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0150 - accuracy: 0.9945 - val_loss: 0.0082 - val_accuracy: 0.9967\n",
      "Epoch 37/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0134 - accuracy: 0.9949 - val_loss: 0.0066 - val_accuracy: 0.9972\n",
      "Epoch 38/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0137 - accuracy: 0.9949 - val_loss: 0.0081 - val_accuracy: 0.9965\n",
      "Epoch 39/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0140 - accuracy: 0.9946 - val_loss: 0.0079 - val_accuracy: 0.9965\n",
      "Epoch 40/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0135 - accuracy: 0.9951 - val_loss: 0.0095 - val_accuracy: 0.9962\n",
      "Epoch 41/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0144 - accuracy: 0.9947 - val_loss: 0.0070 - val_accuracy: 0.9970\n",
      "Epoch 42/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0142 - accuracy: 0.9948 - val_loss: 0.0096 - val_accuracy: 0.9958\n",
      "Epoch 43/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0138 - accuracy: 0.9947 - val_loss: 0.0085 - val_accuracy: 0.9965\n",
      "Epoch 44/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0141 - accuracy: 0.9947 - val_loss: 0.0090 - val_accuracy: 0.9962\n",
      "Epoch 45/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0134 - accuracy: 0.9951 - val_loss: 0.0074 - val_accuracy: 0.9967\n",
      "Epoch 46/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0138 - accuracy: 0.9948 - val_loss: 0.0095 - val_accuracy: 0.9968\n",
      "Epoch 47/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0140 - accuracy: 0.9948 - val_loss: 0.0082 - val_accuracy: 0.9965\n",
      "Epoch 48/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0136 - accuracy: 0.9949 - val_loss: 0.0100 - val_accuracy: 0.9957\n",
      "Epoch 49/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0143 - accuracy: 0.9948 - val_loss: 0.0123 - val_accuracy: 0.9954\n",
      "Epoch 50/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0128 - accuracy: 0.9952 - val_loss: 0.0129 - val_accuracy: 0.9950\n",
      "Epoch 51/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0130 - accuracy: 0.9952 - val_loss: 0.0100 - val_accuracy: 0.9960\n",
      "Epoch 52/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0137 - accuracy: 0.9950 - val_loss: 0.0124 - val_accuracy: 0.9947\n",
      "Epoch 53/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0130 - accuracy: 0.9952 - val_loss: 0.0104 - val_accuracy: 0.9956\n",
      "Epoch 54/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0144 - accuracy: 0.9945 - val_loss: 0.0095 - val_accuracy: 0.9962\n",
      "Epoch 55/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0132 - accuracy: 0.9950 - val_loss: 0.0089 - val_accuracy: 0.9962\n",
      "Epoch 56/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0144 - accuracy: 0.9946 - val_loss: 0.0114 - val_accuracy: 0.9962\n",
      "Epoch 57/60\n",
      "1400/1400 [==============================] - 25s 18ms/step - loss: 0.0131 - accuracy: 0.9950 - val_loss: 0.0104 - val_accuracy: 0.9961\n",
      "Epoch 58/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0132 - accuracy: 0.9951 - val_loss: 0.0107 - val_accuracy: 0.9958\n",
      "Epoch 59/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0129 - accuracy: 0.9952 - val_loss: 0.0113 - val_accuracy: 0.9961\n",
      "Epoch 60/60\n",
      "1400/1400 [==============================] - 26s 18ms/step - loss: 0.0127 - accuracy: 0.9951 - val_loss: 0.0145 - val_accuracy: 0.9943\n",
      "622/622 - 2s - loss: 0.0145 - accuracy: 0.9943 - 2s/epoch - 3ms/step\n",
      "622/622 [==============================] - 2s 3ms/step\n",
      "Completed fold 10/10\n"
     ]
    }
   ],
   "source": [
    "fold_counter = 0\n",
    "\n",
    "# 10-fold Cross Validation\n",
    "for train_index, val_index in skf.split(np.hstack([X_xlead, X_ylead]), y):\n",
    "\n",
    "    # Split data\n",
    "    X_train_xlead, X_val_xlead = X_xlead[train_index], X_xlead[val_index]\n",
    "    X_train_ylead, X_val_ylead = X_ylead[train_index], X_ylead[val_index]\n",
    "    y_train, y_val = y[train_index], y[val_index]\n",
    "    \n",
    "    siamese_model = Model(inputs=[input_left, input_right], outputs=outputs)\n",
    "    siamese_model.compile(optimizer=optimizer, loss=\"binary_crossentropy\", metrics=['accuracy'])\n",
    "\n",
    "    # Train your model\n",
    "    siamese_model.fit(\n",
    "        [X_train_xlead, X_train_ylead], y_train,\n",
    "        validation_data=([X_val_xlead, X_val_ylead], y_val),\n",
    "        epochs=60,\n",
    "        batch_size=128\n",
    "    )\n",
    "    \n",
    "    # Validate the model\n",
    "    # Accuracy, loss\n",
    "    val_loss, val_accuracy = siamese_model.evaluate([X_val_xlead, X_val_ylead], y_val, verbose=2)\n",
    "    fold_accuracies.append(val_accuracy)\n",
    "\n",
    "    pre = siamese_model.predict([X_val_xlead, X_val_ylead])\n",
    "    lower_treshhold = 0.90\n",
    "\n",
    "    model_output = []\n",
    "    for index in range(len(y_val)):\n",
    "        if pre[index] >= lower_treshhold:\n",
    "            model_output.append(1)\n",
    "        else:\n",
    "            model_output.append(0)\n",
    "\n",
    "    tp = 0\n",
    "    tn = 0\n",
    "    fp = 0\n",
    "    fn = 0\n",
    "\n",
    "    for index in range(len(model_output)):\n",
    "        if model_output[index] == 1 and y_val[index] == 1:\n",
    "            tp += 1\n",
    "        if model_output[index] == 1 and y_val[index] == 0:\n",
    "            fp += 1\n",
    "        if model_output[index] == 0 and y_val[index] == 0:\n",
    "            tn += 1\n",
    "        if model_output[index] == 0 and y_val[index] == 1:\n",
    "            fn += 1\n",
    "    \n",
    "    acc_with_treshold = (tp + tn) / len(y_val)\n",
    "    fold_accuracies_with_treshold.append(acc_with_treshold)\n",
    "\n",
    "    # AUPR\n",
    "    y_pred_probs = np.array([[1, 0] if pred == 0 else [0, 1] for pred in model_output])\n",
    "    y_test_onehot = to_categorical(y_val, num_classes=2)\n",
    "    aupr = calculate_aupr(y_test_onehot, y_pred_probs)\n",
    "\n",
    "    # AUC-ROC\n",
    "    y_pred_probs = np.array(model_output)\n",
    "    auc_roc = calculate_auc_roc(y_val, y_pred_probs)\n",
    "    \n",
    "    # Saving the evaluation results\n",
    "    folds_evaluation[fold_counter][\"accuracy\"] =  val_accuracy\n",
    "    folds_evaluation[fold_counter][\"accuracy with treshold\"] = acc_with_treshold\n",
    "    folds_evaluation[fold_counter][\"loss\"] =  val_loss\n",
    "    folds_evaluation[fold_counter][\"AUPR\"] =  aupr\n",
    "    folds_evaluation[fold_counter][\"AUC_ROC\"] = auc_roc\n",
    "    \n",
    "    fold_counter += 1\n",
    "    print(f\"Completed fold {fold_counter}/{n_splits}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: {'accuracy': 0.9673366546630859,\n",
       "  'accuracy with treshold': 0.9713065326633166,\n",
       "  'loss': 0.1364237517118454,\n",
       "  'AUPR': 0.9784798994974875,\n",
       "  'AUC_ROC': 0.9713065326633165},\n",
       " 1: {'accuracy': 0.9759296774864197,\n",
       "  'accuracy with treshold': 0.9792462311557789,\n",
       "  'loss': 0.08240962028503418,\n",
       "  'AUPR': 0.9844346733668341,\n",
       "  'AUC_ROC': 0.9792462311557789},\n",
       " 2: {'accuracy': 0.9788442254066467,\n",
       "  'accuracy with treshold': 0.9835678391959799,\n",
       "  'loss': 0.06689940392971039,\n",
       "  'AUPR': 0.987675879396985,\n",
       "  'AUC_ROC': 0.9835678391959799},\n",
       " 3: {'accuracy': 0.9860301613807678,\n",
       "  'accuracy with treshold': 0.9892964824120603,\n",
       "  'loss': 0.03668554127216339,\n",
       "  'AUPR': 0.9919723618090451,\n",
       "  'AUC_ROC': 0.9892964824120604},\n",
       " 4: {'accuracy': 0.9901005029678345,\n",
       "  'accuracy with treshold': 0.9909045226130653,\n",
       "  'loss': 0.029401760548353195,\n",
       "  'AUPR': 0.993178391959799,\n",
       "  'AUC_ROC': 0.9909045226130653},\n",
       " 5: {'accuracy': 0.991809070110321,\n",
       "  'accuracy with treshold': 0.9910050251256282,\n",
       "  'loss': 0.024776343256235123,\n",
       "  'AUPR': 0.9932537688442211,\n",
       "  'AUC_ROC': 0.991005025125628},\n",
       " 6: {'accuracy': 0.9922612905502319,\n",
       "  'accuracy with treshold': 0.9925628140703517,\n",
       "  'loss': 0.021050631999969482,\n",
       "  'AUPR': 0.9944221105527637,\n",
       "  'AUC_ROC': 0.9925628140703517},\n",
       " 7: {'accuracy': 0.9942713379859924,\n",
       "  'accuracy with treshold': 0.9930150753768844,\n",
       "  'loss': 0.016525249928236008,\n",
       "  'AUPR': 0.9947613065326634,\n",
       "  'AUC_ROC': 0.9930150753768845},\n",
       " 8: {'accuracy': 0.9917588233947754,\n",
       "  'accuracy with treshold': 0.9942713567839196,\n",
       "  'loss': 0.021934209391474724,\n",
       "  'AUPR': 0.9957035175879397,\n",
       "  'AUC_ROC': 0.9942713567839195},\n",
       " 9: {'accuracy': 0.9943215847015381,\n",
       "  'accuracy with treshold': 0.9952763819095477,\n",
       "  'loss': 0.014546476304531097,\n",
       "  'AUPR': 0.9964572864321607,\n",
       "  'AUC_ROC': 0.9952763819095477}}"
      ]
     },
     "execution_count": 345,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "folds_evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10-Fold Cross-Validation Accuracy: 0.9862663328647614\n"
     ]
    }
   ],
   "source": [
    "# Average accuracy across folds\n",
    "average_accuracy = np.mean(fold_accuracies)\n",
    "print(\"10-Fold Cross-Validation Accuracy:\", average_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10-Fold Cross-Validation Accuracy with threshold: 0.9880452261306532\n"
     ]
    }
   ],
   "source": [
    "# Average accuracy with treshold across folds\n",
    "average_accuracy_with_threshold = np.mean(fold_accuracies_with_treshold)\n",
    "print(\"10-Fold Cross-Validation Accuracy with threshold:\", average_accuracy_with_threshold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [],
   "source": [
    "folds_evaluation[\"average_accuracy\"] = average_accuracy\n",
    "folds_evaluation[\"average_accuracy_with_threshold\"] = average_accuracy_with_threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "# Save to a text file\n",
    "with open(\"../6sbf_replayCheck_Siamese_nodataleak.txt\", \"w\") as file:\n",
    "    json.dump(folds_evaluation, file, indent=4) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9673366546630859"
      ]
     },
     "execution_count": 350,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(fold_accuracies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9943215847015381"
      ]
     },
     "execution_count": 351,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(fold_accuracies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9713065326633166"
      ]
     },
     "execution_count": 352,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(fold_accuracies_with_treshold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9952763819095477"
      ]
     },
     "execution_count": 353,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(fold_accuracies_with_treshold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convolotional Network (concate the EKMs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vectorization of EKMs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "def vertorizing_png_imges(address):\n",
    "  # Load the PNG image\n",
    "  image = Image.open(address)\n",
    "\n",
    "  # Convert the image to RGB mode\n",
    "  image = image.convert('RGB')\n",
    "\n",
    "  # Resize the image to match the input size expected by the CNN\n",
    "  desired_width = 31\n",
    "  desired_height = 20\n",
    "  image = image.resize((desired_width, desired_height))\n",
    "\n",
    "  # Convert the image to a NumPy array\n",
    "  image_array = np.array(image)\n",
    "\n",
    "  # Reshape the array to match the input shape expected by the CNN\n",
    "  # image_array = image_array.reshape((1, desired_height, desired_width, 3))\n",
    "\n",
    "  # Normalize the array\n",
    "  image_array = image_array.astype('float32') / 255.0\n",
    "\n",
    "  return image_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "def vectorizing_list_of_tuple_ekms(tuples_ekm_list, name_of_list):\n",
    "    # Get the number of tuples\n",
    "    num_tuple_ekms = len(tuples_ekm_list)\n",
    "    \n",
    "    # Create an empty array with the desired shape\n",
    "    vectorized_ekms = np.empty((num_tuple_ekms, 2, 20, 31, 3), dtype=np.float32)\n",
    "    \n",
    "    # Assuming tuples_ekm_list is a list of tuples, each containing two elements of shape (20, 31, 3)\n",
    "    for idx, (first, second) in enumerate(tuples_ekm_list):\n",
    "        vectorized_ekms[idx, 0] = vertorizing_png_imges(first)\n",
    "        vectorized_ekms[idx, 1] = vertorizing_png_imges(second)\n",
    "        progress_bar(idx, num_tuple_ekms, name_of_list)\n",
    "    \n",
    "    return vectorized_ekms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X!\n",
      "[*************************************************-]\n",
      "198999/199000\n"
     ]
    }
   ],
   "source": [
    "# Vectorizing EKMs\n",
    "X = np.array(X)\n",
    "X = vectorizing_list_of_tuple_ekms(X, \"X!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Concatenate the elements of each tuple horizontally\n",
    "concatenated_X = [np.hstack((tup[0], tup[1])) for tup in X]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3720"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "concatenated_X = np.array(concatenated_X)\n",
    "concatenated_X[0].size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model and prepration of data for fitting them to model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow.keras.models import Sequential \n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout, LSTM, Reshape\n",
    "from tensorflow.keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model architeture"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Improved model: No.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating the CNN model\n",
    "model = Sequential([\n",
    "      Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=(20, 62, 3)),\n",
    "      MaxPooling2D(pool_size=(2, 2)),\n",
    "      Conv2D(64, (3, 3), activation='relu', padding='same'),\n",
    "      \n",
    "      # Added fewer pooling layers to avoid excessive reduction of dimensions\n",
    "      Conv2D(128, (3, 3), activation='relu', padding='same'),\n",
    "      MaxPooling2D(pool_size=(2, 2)),\n",
    "      \n",
    "      Conv2D(256, (3, 3), activation='relu', padding='same'),\n",
    "      MaxPooling2D(pool_size=(2, 2)),\n",
    "\n",
    "      Dropout(0.7),\n",
    "\n",
    "      Flatten(),\n",
    "      Dense(256, activation='relu'),\n",
    "      Dense(128, activation='relu'),\n",
    "      Dense(64, activation='relu'),\n",
    "      Dense(32, activation='relu'),\n",
    "      Dense(8, activation='relu'),\n",
    "      Dense(2, activation='softmax')\n",
    "])\n",
    "\n",
    "# Setting Adam optimizer\n",
    "optimizer = Adam(learning_rate=0.001)\n",
    "\n",
    "# Compileing the model with the optimizer\n",
    "model.compile(optimizer=optimizer, loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train the model\n",
    "model.fit(X_train, y_train, epochs=60, batch_size=128)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resnet model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.applications import ResNet50\n",
    "from tensorflow.keras.layers import Input, Dense, Flatten, GlobalAveragePooling2D\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.image import resize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_data(X, target_size=(32, 128)):\n",
    "    # Resize or pad images to the target size\n",
    "    resized_images = np.array([resize(img, target_size).numpy() for img in X])\n",
    "    return resized_images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_resnet_model(input_shape):\n",
    "    base_model = ResNet50(include_top=False, weights=None, input_shape=input_shape)\n",
    "    output = GlobalAveragePooling2D()(base_model.output)\n",
    "    output = Dense(256, activation='relu')(output)\n",
    "    output = Dense(128, activation='relu')(output)\n",
    "    output = Dense(1, activation='sigmoid', name='Similarity')(output)\n",
    "    model = Model(inputs=base_model.input, outputs=output)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_3\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                Output Shape                 Param #   Connected to                  \n",
      "==================================================================================================\n",
      " input_3 (InputLayer)        [(None, 32, 128, 3)]         0         []                            \n",
      "                                                                                                  \n",
      " conv1_pad (ZeroPadding2D)   (None, 38, 134, 3)           0         ['input_3[0][0]']             \n",
      "                                                                                                  \n",
      " conv1_conv (Conv2D)         (None, 16, 64, 64)           9472      ['conv1_pad[0][0]']           \n",
      "                                                                                                  \n",
      " conv1_bn (BatchNormalizati  (None, 16, 64, 64)           256       ['conv1_conv[0][0]']          \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv1_relu (Activation)     (None, 16, 64, 64)           0         ['conv1_bn[0][0]']            \n",
      "                                                                                                  \n",
      " pool1_pad (ZeroPadding2D)   (None, 18, 66, 64)           0         ['conv1_relu[0][0]']          \n",
      "                                                                                                  \n",
      " pool1_pool (MaxPooling2D)   (None, 8, 32, 64)            0         ['pool1_pad[0][0]']           \n",
      "                                                                                                  \n",
      " conv2_block1_1_conv (Conv2  (None, 8, 32, 64)            4160      ['pool1_pool[0][0]']          \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block1_1_bn (BatchNo  (None, 8, 32, 64)            256       ['conv2_block1_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv2_block1_1_relu (Activ  (None, 8, 32, 64)            0         ['conv2_block1_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv2_block1_2_conv (Conv2  (None, 8, 32, 64)            36928     ['conv2_block1_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block1_2_bn (BatchNo  (None, 8, 32, 64)            256       ['conv2_block1_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv2_block1_2_relu (Activ  (None, 8, 32, 64)            0         ['conv2_block1_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv2_block1_0_conv (Conv2  (None, 8, 32, 256)           16640     ['pool1_pool[0][0]']          \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block1_3_conv (Conv2  (None, 8, 32, 256)           16640     ['conv2_block1_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block1_0_bn (BatchNo  (None, 8, 32, 256)           1024      ['conv2_block1_0_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv2_block1_3_bn (BatchNo  (None, 8, 32, 256)           1024      ['conv2_block1_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv2_block1_add (Add)      (None, 8, 32, 256)           0         ['conv2_block1_0_bn[0][0]',   \n",
      "                                                                     'conv2_block1_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv2_block1_out (Activati  (None, 8, 32, 256)           0         ['conv2_block1_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv2_block2_1_conv (Conv2  (None, 8, 32, 64)            16448     ['conv2_block1_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block2_1_bn (BatchNo  (None, 8, 32, 64)            256       ['conv2_block2_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv2_block2_1_relu (Activ  (None, 8, 32, 64)            0         ['conv2_block2_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv2_block2_2_conv (Conv2  (None, 8, 32, 64)            36928     ['conv2_block2_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block2_2_bn (BatchNo  (None, 8, 32, 64)            256       ['conv2_block2_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv2_block2_2_relu (Activ  (None, 8, 32, 64)            0         ['conv2_block2_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv2_block2_3_conv (Conv2  (None, 8, 32, 256)           16640     ['conv2_block2_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block2_3_bn (BatchNo  (None, 8, 32, 256)           1024      ['conv2_block2_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv2_block2_add (Add)      (None, 8, 32, 256)           0         ['conv2_block1_out[0][0]',    \n",
      "                                                                     'conv2_block2_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv2_block2_out (Activati  (None, 8, 32, 256)           0         ['conv2_block2_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv2_block3_1_conv (Conv2  (None, 8, 32, 64)            16448     ['conv2_block2_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block3_1_bn (BatchNo  (None, 8, 32, 64)            256       ['conv2_block3_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv2_block3_1_relu (Activ  (None, 8, 32, 64)            0         ['conv2_block3_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv2_block3_2_conv (Conv2  (None, 8, 32, 64)            36928     ['conv2_block3_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block3_2_bn (BatchNo  (None, 8, 32, 64)            256       ['conv2_block3_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv2_block3_2_relu (Activ  (None, 8, 32, 64)            0         ['conv2_block3_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv2_block3_3_conv (Conv2  (None, 8, 32, 256)           16640     ['conv2_block3_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block3_3_bn (BatchNo  (None, 8, 32, 256)           1024      ['conv2_block3_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv2_block3_add (Add)      (None, 8, 32, 256)           0         ['conv2_block2_out[0][0]',    \n",
      "                                                                     'conv2_block3_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv2_block3_out (Activati  (None, 8, 32, 256)           0         ['conv2_block3_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv3_block1_1_conv (Conv2  (None, 4, 16, 128)           32896     ['conv2_block3_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block1_1_bn (BatchNo  (None, 4, 16, 128)           512       ['conv3_block1_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block1_1_relu (Activ  (None, 4, 16, 128)           0         ['conv3_block1_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv3_block1_2_conv (Conv2  (None, 4, 16, 128)           147584    ['conv3_block1_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block1_2_bn (BatchNo  (None, 4, 16, 128)           512       ['conv3_block1_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block1_2_relu (Activ  (None, 4, 16, 128)           0         ['conv3_block1_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv3_block1_0_conv (Conv2  (None, 4, 16, 512)           131584    ['conv2_block3_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block1_3_conv (Conv2  (None, 4, 16, 512)           66048     ['conv3_block1_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block1_0_bn (BatchNo  (None, 4, 16, 512)           2048      ['conv3_block1_0_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block1_3_bn (BatchNo  (None, 4, 16, 512)           2048      ['conv3_block1_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block1_add (Add)      (None, 4, 16, 512)           0         ['conv3_block1_0_bn[0][0]',   \n",
      "                                                                     'conv3_block1_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv3_block1_out (Activati  (None, 4, 16, 512)           0         ['conv3_block1_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv3_block2_1_conv (Conv2  (None, 4, 16, 128)           65664     ['conv3_block1_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block2_1_bn (BatchNo  (None, 4, 16, 128)           512       ['conv3_block2_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block2_1_relu (Activ  (None, 4, 16, 128)           0         ['conv3_block2_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv3_block2_2_conv (Conv2  (None, 4, 16, 128)           147584    ['conv3_block2_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block2_2_bn (BatchNo  (None, 4, 16, 128)           512       ['conv3_block2_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block2_2_relu (Activ  (None, 4, 16, 128)           0         ['conv3_block2_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv3_block2_3_conv (Conv2  (None, 4, 16, 512)           66048     ['conv3_block2_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block2_3_bn (BatchNo  (None, 4, 16, 512)           2048      ['conv3_block2_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block2_add (Add)      (None, 4, 16, 512)           0         ['conv3_block1_out[0][0]',    \n",
      "                                                                     'conv3_block2_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv3_block2_out (Activati  (None, 4, 16, 512)           0         ['conv3_block2_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv3_block3_1_conv (Conv2  (None, 4, 16, 128)           65664     ['conv3_block2_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block3_1_bn (BatchNo  (None, 4, 16, 128)           512       ['conv3_block3_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block3_1_relu (Activ  (None, 4, 16, 128)           0         ['conv3_block3_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv3_block3_2_conv (Conv2  (None, 4, 16, 128)           147584    ['conv3_block3_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block3_2_bn (BatchNo  (None, 4, 16, 128)           512       ['conv3_block3_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block3_2_relu (Activ  (None, 4, 16, 128)           0         ['conv3_block3_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv3_block3_3_conv (Conv2  (None, 4, 16, 512)           66048     ['conv3_block3_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block3_3_bn (BatchNo  (None, 4, 16, 512)           2048      ['conv3_block3_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block3_add (Add)      (None, 4, 16, 512)           0         ['conv3_block2_out[0][0]',    \n",
      "                                                                     'conv3_block3_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv3_block3_out (Activati  (None, 4, 16, 512)           0         ['conv3_block3_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv3_block4_1_conv (Conv2  (None, 4, 16, 128)           65664     ['conv3_block3_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block4_1_bn (BatchNo  (None, 4, 16, 128)           512       ['conv3_block4_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block4_1_relu (Activ  (None, 4, 16, 128)           0         ['conv3_block4_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv3_block4_2_conv (Conv2  (None, 4, 16, 128)           147584    ['conv3_block4_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block4_2_bn (BatchNo  (None, 4, 16, 128)           512       ['conv3_block4_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block4_2_relu (Activ  (None, 4, 16, 128)           0         ['conv3_block4_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv3_block4_3_conv (Conv2  (None, 4, 16, 512)           66048     ['conv3_block4_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block4_3_bn (BatchNo  (None, 4, 16, 512)           2048      ['conv3_block4_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block4_add (Add)      (None, 4, 16, 512)           0         ['conv3_block3_out[0][0]',    \n",
      "                                                                     'conv3_block4_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv3_block4_out (Activati  (None, 4, 16, 512)           0         ['conv3_block4_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv4_block1_1_conv (Conv2  (None, 2, 8, 256)            131328    ['conv3_block4_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block1_1_bn (BatchNo  (None, 2, 8, 256)            1024      ['conv4_block1_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block1_1_relu (Activ  (None, 2, 8, 256)            0         ['conv4_block1_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv4_block1_2_conv (Conv2  (None, 2, 8, 256)            590080    ['conv4_block1_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block1_2_bn (BatchNo  (None, 2, 8, 256)            1024      ['conv4_block1_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block1_2_relu (Activ  (None, 2, 8, 256)            0         ['conv4_block1_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv4_block1_0_conv (Conv2  (None, 2, 8, 1024)           525312    ['conv3_block4_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block1_3_conv (Conv2  (None, 2, 8, 1024)           263168    ['conv4_block1_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block1_0_bn (BatchNo  (None, 2, 8, 1024)           4096      ['conv4_block1_0_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block1_3_bn (BatchNo  (None, 2, 8, 1024)           4096      ['conv4_block1_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block1_add (Add)      (None, 2, 8, 1024)           0         ['conv4_block1_0_bn[0][0]',   \n",
      "                                                                     'conv4_block1_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv4_block1_out (Activati  (None, 2, 8, 1024)           0         ['conv4_block1_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv4_block2_1_conv (Conv2  (None, 2, 8, 256)            262400    ['conv4_block1_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block2_1_bn (BatchNo  (None, 2, 8, 256)            1024      ['conv4_block2_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block2_1_relu (Activ  (None, 2, 8, 256)            0         ['conv4_block2_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv4_block2_2_conv (Conv2  (None, 2, 8, 256)            590080    ['conv4_block2_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block2_2_bn (BatchNo  (None, 2, 8, 256)            1024      ['conv4_block2_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block2_2_relu (Activ  (None, 2, 8, 256)            0         ['conv4_block2_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv4_block2_3_conv (Conv2  (None, 2, 8, 1024)           263168    ['conv4_block2_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block2_3_bn (BatchNo  (None, 2, 8, 1024)           4096      ['conv4_block2_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block2_add (Add)      (None, 2, 8, 1024)           0         ['conv4_block1_out[0][0]',    \n",
      "                                                                     'conv4_block2_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv4_block2_out (Activati  (None, 2, 8, 1024)           0         ['conv4_block2_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv4_block3_1_conv (Conv2  (None, 2, 8, 256)            262400    ['conv4_block2_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block3_1_bn (BatchNo  (None, 2, 8, 256)            1024      ['conv4_block3_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block3_1_relu (Activ  (None, 2, 8, 256)            0         ['conv4_block3_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv4_block3_2_conv (Conv2  (None, 2, 8, 256)            590080    ['conv4_block3_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block3_2_bn (BatchNo  (None, 2, 8, 256)            1024      ['conv4_block3_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block3_2_relu (Activ  (None, 2, 8, 256)            0         ['conv4_block3_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv4_block3_3_conv (Conv2  (None, 2, 8, 1024)           263168    ['conv4_block3_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block3_3_bn (BatchNo  (None, 2, 8, 1024)           4096      ['conv4_block3_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block3_add (Add)      (None, 2, 8, 1024)           0         ['conv4_block2_out[0][0]',    \n",
      "                                                                     'conv4_block3_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv4_block3_out (Activati  (None, 2, 8, 1024)           0         ['conv4_block3_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv4_block4_1_conv (Conv2  (None, 2, 8, 256)            262400    ['conv4_block3_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block4_1_bn (BatchNo  (None, 2, 8, 256)            1024      ['conv4_block4_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block4_1_relu (Activ  (None, 2, 8, 256)            0         ['conv4_block4_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv4_block4_2_conv (Conv2  (None, 2, 8, 256)            590080    ['conv4_block4_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block4_2_bn (BatchNo  (None, 2, 8, 256)            1024      ['conv4_block4_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block4_2_relu (Activ  (None, 2, 8, 256)            0         ['conv4_block4_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv4_block4_3_conv (Conv2  (None, 2, 8, 1024)           263168    ['conv4_block4_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block4_3_bn (BatchNo  (None, 2, 8, 1024)           4096      ['conv4_block4_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block4_add (Add)      (None, 2, 8, 1024)           0         ['conv4_block3_out[0][0]',    \n",
      "                                                                     'conv4_block4_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv4_block4_out (Activati  (None, 2, 8, 1024)           0         ['conv4_block4_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv4_block5_1_conv (Conv2  (None, 2, 8, 256)            262400    ['conv4_block4_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block5_1_bn (BatchNo  (None, 2, 8, 256)            1024      ['conv4_block5_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block5_1_relu (Activ  (None, 2, 8, 256)            0         ['conv4_block5_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv4_block5_2_conv (Conv2  (None, 2, 8, 256)            590080    ['conv4_block5_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block5_2_bn (BatchNo  (None, 2, 8, 256)            1024      ['conv4_block5_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block5_2_relu (Activ  (None, 2, 8, 256)            0         ['conv4_block5_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv4_block5_3_conv (Conv2  (None, 2, 8, 1024)           263168    ['conv4_block5_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block5_3_bn (BatchNo  (None, 2, 8, 1024)           4096      ['conv4_block5_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block5_add (Add)      (None, 2, 8, 1024)           0         ['conv4_block4_out[0][0]',    \n",
      "                                                                     'conv4_block5_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv4_block5_out (Activati  (None, 2, 8, 1024)           0         ['conv4_block5_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv4_block6_1_conv (Conv2  (None, 2, 8, 256)            262400    ['conv4_block5_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block6_1_bn (BatchNo  (None, 2, 8, 256)            1024      ['conv4_block6_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block6_1_relu (Activ  (None, 2, 8, 256)            0         ['conv4_block6_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv4_block6_2_conv (Conv2  (None, 2, 8, 256)            590080    ['conv4_block6_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block6_2_bn (BatchNo  (None, 2, 8, 256)            1024      ['conv4_block6_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block6_2_relu (Activ  (None, 2, 8, 256)            0         ['conv4_block6_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv4_block6_3_conv (Conv2  (None, 2, 8, 1024)           263168    ['conv4_block6_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block6_3_bn (BatchNo  (None, 2, 8, 1024)           4096      ['conv4_block6_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block6_add (Add)      (None, 2, 8, 1024)           0         ['conv4_block5_out[0][0]',    \n",
      "                                                                     'conv4_block6_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv4_block6_out (Activati  (None, 2, 8, 1024)           0         ['conv4_block6_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv5_block1_1_conv (Conv2  (None, 1, 4, 512)            524800    ['conv4_block6_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block1_1_bn (BatchNo  (None, 1, 4, 512)            2048      ['conv5_block1_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv5_block1_1_relu (Activ  (None, 1, 4, 512)            0         ['conv5_block1_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv5_block1_2_conv (Conv2  (None, 1, 4, 512)            2359808   ['conv5_block1_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block1_2_bn (BatchNo  (None, 1, 4, 512)            2048      ['conv5_block1_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv5_block1_2_relu (Activ  (None, 1, 4, 512)            0         ['conv5_block1_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv5_block1_0_conv (Conv2  (None, 1, 4, 2048)           2099200   ['conv4_block6_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block1_3_conv (Conv2  (None, 1, 4, 2048)           1050624   ['conv5_block1_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block1_0_bn (BatchNo  (None, 1, 4, 2048)           8192      ['conv5_block1_0_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv5_block1_3_bn (BatchNo  (None, 1, 4, 2048)           8192      ['conv5_block1_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv5_block1_add (Add)      (None, 1, 4, 2048)           0         ['conv5_block1_0_bn[0][0]',   \n",
      "                                                                     'conv5_block1_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv5_block1_out (Activati  (None, 1, 4, 2048)           0         ['conv5_block1_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv5_block2_1_conv (Conv2  (None, 1, 4, 512)            1049088   ['conv5_block1_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block2_1_bn (BatchNo  (None, 1, 4, 512)            2048      ['conv5_block2_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv5_block2_1_relu (Activ  (None, 1, 4, 512)            0         ['conv5_block2_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv5_block2_2_conv (Conv2  (None, 1, 4, 512)            2359808   ['conv5_block2_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block2_2_bn (BatchNo  (None, 1, 4, 512)            2048      ['conv5_block2_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv5_block2_2_relu (Activ  (None, 1, 4, 512)            0         ['conv5_block2_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv5_block2_3_conv (Conv2  (None, 1, 4, 2048)           1050624   ['conv5_block2_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block2_3_bn (BatchNo  (None, 1, 4, 2048)           8192      ['conv5_block2_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv5_block2_add (Add)      (None, 1, 4, 2048)           0         ['conv5_block1_out[0][0]',    \n",
      "                                                                     'conv5_block2_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv5_block2_out (Activati  (None, 1, 4, 2048)           0         ['conv5_block2_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv5_block3_1_conv (Conv2  (None, 1, 4, 512)            1049088   ['conv5_block2_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block3_1_bn (BatchNo  (None, 1, 4, 512)            2048      ['conv5_block3_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv5_block3_1_relu (Activ  (None, 1, 4, 512)            0         ['conv5_block3_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv5_block3_2_conv (Conv2  (None, 1, 4, 512)            2359808   ['conv5_block3_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block3_2_bn (BatchNo  (None, 1, 4, 512)            2048      ['conv5_block3_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv5_block3_2_relu (Activ  (None, 1, 4, 512)            0         ['conv5_block3_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv5_block3_3_conv (Conv2  (None, 1, 4, 2048)           1050624   ['conv5_block3_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block3_3_bn (BatchNo  (None, 1, 4, 2048)           8192      ['conv5_block3_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv5_block3_add (Add)      (None, 1, 4, 2048)           0         ['conv5_block2_out[0][0]',    \n",
      "                                                                     'conv5_block3_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv5_block3_out (Activati  (None, 1, 4, 2048)           0         ['conv5_block3_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " global_average_pooling2d_1  (None, 2048)                 0         ['conv5_block3_out[0][0]']    \n",
      "  (GlobalAveragePooling2D)                                                                        \n",
      "                                                                                                  \n",
      " dense_33 (Dense)            (None, 256)                  524544    ['global_average_pooling2d_1[0\n",
      "                                                                    ][0]']                        \n",
      "                                                                                                  \n",
      " dense_34 (Dense)            (None, 128)                  32896     ['dense_33[0][0]']            \n",
      "                                                                                                  \n",
      " Similarity (Dense)          (None, 1)                    129       ['dense_34[0][0]']            \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 24145281 (92.11 MB)\n",
      "Trainable params: 24092161 (91.90 MB)\n",
      "Non-trainable params: 53120 (207.50 KB)\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Adjust input shape for resized images (resnet model requires it!)\n",
    "input_shape = (32, 128, 3)\n",
    "\n",
    "# Preprocess data to match model input shape\n",
    "X_preprocessed = preprocess_data(X, target_size=(32, 128))\n",
    "                          \n",
    "# Create the model\n",
    "resnet_model = create_resnet_model(input_shape)\n",
    "\n",
    "# Compile the model\n",
    "resnet_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# Summary\n",
    "resnet_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting train/test data (EKMs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting train and test data by proportion of 80/20\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Split the data into training and validation sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_preprocessed, y, test_size=0.2, random_state=42, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/60\n",
      "3980/3980 [==============================] - 1176s 295ms/step - loss: 0.2782 - accuracy: 0.8730 - val_loss: 0.1466 - val_accuracy: 0.9498\n",
      "Epoch 2/60\n",
      "3980/3980 [==============================] - 1143s 287ms/step - loss: 0.1273 - accuracy: 0.9552 - val_loss: 0.0811 - val_accuracy: 0.9720\n",
      "Epoch 3/60\n",
      "3980/3980 [==============================] - 1142s 287ms/step - loss: 0.0719 - accuracy: 0.9741 - val_loss: 0.0639 - val_accuracy: 0.9761\n",
      "Epoch 4/60\n",
      "3980/3980 [==============================] - 1138s 286ms/step - loss: 0.0571 - accuracy: 0.9788 - val_loss: 0.0558 - val_accuracy: 0.9792\n",
      "Epoch 5/60\n",
      "3980/3980 [==============================] - 1139s 286ms/step - loss: 0.0494 - accuracy: 0.9806 - val_loss: 0.0530 - val_accuracy: 0.9812\n",
      "Epoch 6/60\n",
      "3980/3980 [==============================] - 1138s 286ms/step - loss: 0.0435 - accuracy: 0.9825 - val_loss: 0.0485 - val_accuracy: 0.9800\n",
      "Epoch 7/60\n",
      "3980/3980 [==============================] - 1137s 286ms/step - loss: 0.0384 - accuracy: 0.9841 - val_loss: 0.0401 - val_accuracy: 0.9837\n",
      "Epoch 8/60\n",
      "3980/3980 [==============================] - 1137s 286ms/step - loss: 0.0359 - accuracy: 0.9849 - val_loss: 0.0476 - val_accuracy: 0.9824\n",
      "Epoch 9/60\n",
      "3980/3980 [==============================] - 1137s 286ms/step - loss: 0.0326 - accuracy: 0.9859 - val_loss: 0.0424 - val_accuracy: 0.9828\n",
      "Epoch 10/60\n",
      "3980/3980 [==============================] - 1137s 286ms/step - loss: 0.0310 - accuracy: 0.9865 - val_loss: 0.0430 - val_accuracy: 0.9823\n",
      "Epoch 11/60\n",
      "3980/3980 [==============================] - 1138s 286ms/step - loss: 0.0285 - accuracy: 0.9873 - val_loss: 0.0470 - val_accuracy: 0.9814\n",
      "Epoch 12/60\n",
      "3980/3980 [==============================] - 1138s 286ms/step - loss: 0.0273 - accuracy: 0.9878 - val_loss: 0.0410 - val_accuracy: 0.9838\n",
      "Epoch 13/60\n",
      "3980/3980 [==============================] - 1138s 286ms/step - loss: 0.0251 - accuracy: 0.9882 - val_loss: 0.0356 - val_accuracy: 0.9860\n",
      "Epoch 14/60\n",
      "3980/3980 [==============================] - 1138s 286ms/step - loss: 0.0240 - accuracy: 0.9884 - val_loss: 0.0372 - val_accuracy: 0.9854\n",
      "Epoch 15/60\n",
      "3980/3980 [==============================] - 1164s 293ms/step - loss: 0.0230 - accuracy: 0.9891 - val_loss: 0.0425 - val_accuracy: 0.9842\n",
      "Epoch 16/60\n",
      "1796/3980 [============>.................] - ETA: 10:20 - loss: 0.0204 - accuracy: 0.9903"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[152], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Train the model\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[43mresnet_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m32\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m60\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalidation_split\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.2\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/keras/src/utils/traceback_utils.py:65\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     63\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     64\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 65\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     66\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m     67\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/keras/src/engine/training.py:1742\u001b[0m, in \u001b[0;36mModel.fit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1734\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m tf\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mexperimental\u001b[38;5;241m.\u001b[39mTrace(\n\u001b[1;32m   1735\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   1736\u001b[0m     epoch_num\u001b[38;5;241m=\u001b[39mepoch,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1739\u001b[0m     _r\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m   1740\u001b[0m ):\n\u001b[1;32m   1741\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_begin(step)\n\u001b[0;32m-> 1742\u001b[0m     tmp_logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1743\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m data_handler\u001b[38;5;241m.\u001b[39mshould_sync:\n\u001b[1;32m   1744\u001b[0m         context\u001b[38;5;241m.\u001b[39masync_wait()\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/tensorflow/python/util/traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 150\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    152\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:825\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    822\u001b[0m compiler \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxla\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnonXla\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    824\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OptionalXlaContext(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile):\n\u001b[0;32m--> 825\u001b[0m   result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    827\u001b[0m new_tracing_count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexperimental_get_tracing_count()\n\u001b[1;32m    828\u001b[0m without_tracing \u001b[38;5;241m=\u001b[39m (tracing_count \u001b[38;5;241m==\u001b[39m new_tracing_count)\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:857\u001b[0m, in \u001b[0;36mFunction._call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    854\u001b[0m   \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[1;32m    855\u001b[0m   \u001b[38;5;66;03m# In this case we have created variables on the first call, so we run the\u001b[39;00m\n\u001b[1;32m    856\u001b[0m   \u001b[38;5;66;03m# defunned version which is guaranteed to never create variables.\u001b[39;00m\n\u001b[0;32m--> 857\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_no_variable_creation_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# pylint: disable=not-callable\u001b[39;00m\n\u001b[1;32m    858\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_variable_creation_fn \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    859\u001b[0m   \u001b[38;5;66;03m# Release the lock early so that multiple threads can perform the call\u001b[39;00m\n\u001b[1;32m    860\u001b[0m   \u001b[38;5;66;03m# in parallel.\u001b[39;00m\n\u001b[1;32m    861\u001b[0m   \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/tensorflow/python/eager/polymorphic_function/tracing_compiler.py:148\u001b[0m, in \u001b[0;36mTracingCompiler.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    145\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n\u001b[1;32m    146\u001b[0m   (concrete_function,\n\u001b[1;32m    147\u001b[0m    filtered_flat_args) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_maybe_define_function(args, kwargs)\n\u001b[0;32m--> 148\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mconcrete_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_flat\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    149\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfiltered_flat_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcaptured_inputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconcrete_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcaptured_inputs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/tensorflow/python/eager/polymorphic_function/monomorphic_function.py:1349\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[0;34m(self, args, captured_inputs)\u001b[0m\n\u001b[1;32m   1345\u001b[0m possible_gradient_type \u001b[38;5;241m=\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[1;32m   1346\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (possible_gradient_type \u001b[38;5;241m==\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[1;32m   1347\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m executing_eagerly):\n\u001b[1;32m   1348\u001b[0m   \u001b[38;5;66;03m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[0;32m-> 1349\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_build_call_outputs(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_inference_function\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m   1350\u001b[0m forward_backward \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[1;32m   1351\u001b[0m     args,\n\u001b[1;32m   1352\u001b[0m     possible_gradient_type,\n\u001b[1;32m   1353\u001b[0m     executing_eagerly)\n\u001b[1;32m   1354\u001b[0m forward_function, args_with_tangents \u001b[38;5;241m=\u001b[39m forward_backward\u001b[38;5;241m.\u001b[39mforward()\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/tensorflow/python/eager/polymorphic_function/atomic_function.py:196\u001b[0m, in \u001b[0;36mAtomicFunction.__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m    194\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m record\u001b[38;5;241m.\u001b[39mstop_recording():\n\u001b[1;32m    195\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mexecuting_eagerly():\n\u001b[0;32m--> 196\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_bound_context\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    197\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    198\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    199\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunction_type\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mflat_outputs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    200\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    201\u001b[0m   \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    202\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m make_call_op_in_graph(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28mlist\u001b[39m(args))\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/tensorflow/python/eager/context.py:1457\u001b[0m, in \u001b[0;36mContext.call_function\u001b[0;34m(self, name, tensor_inputs, num_outputs)\u001b[0m\n\u001b[1;32m   1455\u001b[0m cancellation_context \u001b[38;5;241m=\u001b[39m cancellation\u001b[38;5;241m.\u001b[39mcontext()\n\u001b[1;32m   1456\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cancellation_context \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1457\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m \u001b[43mexecute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1458\u001b[0m \u001b[43m      \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mutf-8\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1459\u001b[0m \u001b[43m      \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1460\u001b[0m \u001b[43m      \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtensor_inputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1461\u001b[0m \u001b[43m      \u001b[49m\u001b[43mattrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1462\u001b[0m \u001b[43m      \u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1463\u001b[0m \u001b[43m  \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1464\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1465\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute_with_cancellation(\n\u001b[1;32m   1466\u001b[0m       name\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m   1467\u001b[0m       num_outputs\u001b[38;5;241m=\u001b[39mnum_outputs,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1471\u001b[0m       cancellation_manager\u001b[38;5;241m=\u001b[39mcancellation_context,\n\u001b[1;32m   1472\u001b[0m   )\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/tensorflow/python/eager/execute.py:53\u001b[0m, in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     51\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m     52\u001b[0m   ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[0;32m---> 53\u001b[0m   tensors \u001b[38;5;241m=\u001b[39m \u001b[43mpywrap_tfe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTFE_Py_Execute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_handle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     54\u001b[0m \u001b[43m                                      \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     55\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m     56\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "resnet_model.fit(X_train, y_train, batch_size=32, epochs=15, validation_split=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = resnet_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accuracy, loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1244/1244 - 62s - loss: 0.0401 - accuracy: 0.9856 - 62s/epoch - 50ms/step\n",
      "Test Loss: 0.0401\n",
      "Test Accuracy: 0.9856\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model on the test set\n",
    "test_loss, test_accuracy = model.evaluate(X_test, y_test, verbose=2)\n",
    "print(f'Test Loss: {test_loss:.4f}')\n",
    "print(f'Test Accuracy: {test_accuracy:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### AUPR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_recall_curve, auc\n",
    "from tensorflow.keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_aupr(y_true, y_pred_probs):\n",
    "    \"\"\"\n",
    "    Calculate the Area Under the Precision-Recall Curve (AUPR).\n",
    "    \"\"\"\n",
    "    precision, recall, _ = precision_recall_curve(y_true.ravel(), y_pred_probs.ravel())\n",
    "    aupr = auc(recall, precision)\n",
    "    return aupr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1244/1244 [==============================] - 65s 52ms/step\n",
      "AUPR: 0.9990\n"
     ]
    }
   ],
   "source": [
    "y_pred_probs = model.predict(X_test)\n",
    "# y_test_onehot = to_categorical(y_test, num_classes=2)\n",
    "\n",
    "aupr = calculate_aupr(y_test, y_pred_probs)\n",
    "print(f\"AUPR: {aupr:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### AUC-ROC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "def calculate_auc_roc(y_true, y_pred_probs):\n",
    "    \"\"\"\n",
    "    Calculate the Area Under the Receiver Operating Characteristic Curve (AUC-ROC).\n",
    "    \"\"\"\n",
    "    auc_roc = roc_auc_score(y_true, y_pred_probs, multi_class='ovr')  # 'ovr' for one-vs-rest\n",
    "    return auc_roc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1244/1244 [==============================] - 64s 52ms/step\n",
      "AUC-ROC: 0.9991\n"
     ]
    }
   ],
   "source": [
    "y_pred_probs = model.predict(X_test)\n",
    "auc_roc = calculate_auc_roc(y_test, y_pred_probs)\n",
    "print(f\"AUC-ROC: {auc_roc:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "def calculate_confusion_matrix(y_true, y_pred):\n",
    "    \"\"\"\n",
    "    Calculate the confusion matrix.\n",
    "    \"\"\"\n",
    "    cm = confusion_matrix(y_true, y_pred)\n",
    "    return cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1244/1244 [==============================] - 64s 51ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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Pz0/79u3TlClTFBoaqo8++sg6ZseOHerQoYO6d++uAwcOqFWrVmrVqpWOHDmSrudhzj0AAADsJ5OtltOkSRM1adLkvse9vb1tXn/zzTeqX7++SpcubbM/b968qcbetXjxYiUlJWn+/PlydXXVU089pcjISE2fPl29evWSJM2aNUuNGzfWkCFDJEnjx49XeHi4Zs+erXnz5qX5eTLXuwsAAAA8osTERCUkJNhsiYmJj3zduLg4rVmzRt27d091bNKkSSpYsKCqVKmiKVOm6Pbt29ZjERERqlOnjlxdXa37goKCdPz4cV2+fNk6pmHDhjbXDAoKUkRERLpqpLkHAACA/Vgspm9hYWHy9PS02cLCwh659IULFypv3rxq06aNzf633npLS5cu1ZYtW9S7d29NnDhRQ4cOtR6PjY2Vl5eXzTl3X8fGxv7jmLvH04ppOQAAALAfO0zLGT58uEJCQmz2ubm5PfJ158+fr06dOsnd3d1m/1/vVbFiRbm6uqp3794KCwvLkPumB809AAAAnIqbm1uGN9Xff/+9jh8/rmXLlj1wbI0aNXT79m1FR0erXLly8vb2VlxcnM2Yu6/vztO/35j7zeO/H6blAAAAwH7sMC3HDJ9++qmqVaumSpUqPXBsZGSkXFxcVKRIEUlSYGCgtm/frlu3blnHhIeHq1y5csqfP791zKZNm2yuEx4ersDAwHTVSXMPAACAx9bVq1cVGRmpyMhISVJUVJQiIyMVExNjHZOQkKAVK1aoR48eqc6PiIjQzJkzdfDgQZ0+fVqLFy/WwIED1blzZ2vj3rFjR7m6uqp79+46evSoli1bplmzZtlM5+nfv7/WrVunadOm6dixYwoNDdXevXvVt2/fdD0P03IAAABgP5lsKcy9e/eqfv361td3G+6uXbtqwYIFkqSlS5fKMAx16NAh1flubm5aunSpQkNDlZiYqFKlSmngwIE2jbunp6c2bNig4OBgVatWTYUKFdLo0aOty2BKUs2aNbVkyRKNHDlS77zzjvz9/bVq1So9/fTT6Xoei2EYRrrOyAJyVknfbzgAYIbLe2Y7ugQAkHsmi3Jztv7E9Hvc+Dp1wv64yGT/uAEAAODUTJoTjzsy19+LAAAAAHhoJPcAAACwGwvJvalI7gEAAAAnQXIPAAAAuyG5NxfJPQAAAOAkSO4BAABgPwT3piK5BwAAAJwEyT0AAADshjn35iK5BwAAAJwEyT0AAADshuTeXCT3AAAAgJMguQcAAIDdkNybi+QeAAAAcBIk9wAAALAbkntzkdwDAAAAToLkHgAAAPZDcG8qknsAAADASZDcAwAAwG6Yc28uknsAAADASZDcAwAAwG5I7s1Fcg8AAAA4CZJ7AAAA2A3JvblI7gEAAAAnQXIPAAAAuyG5NxfJPQAAAOAkSO4BAABgPwT3piK5BwAAAJwEyT0AAADshjn35iK5BwAAAJwEyT0AAADshuTeXCT3AAAAgJMguQcAAIDdkNybi+QeAAAAcBIk9wAAALAfgntTkdwDAAAAToLkHgAAAHbDnHtzkdwDAAAAToLkHgAAAHZDcm8uknsAAADASTg0uU9KStKqVasUERGh2NhYSZK3t7dq1qypli1bytXV1ZHlAQAAIIOR3JvLYcn9yZMnVb58eXXt2lUHDhxQSkqKUlJSdODAAXXp0kVPPfWUTp486ajyAAAAgCzHYcn9G2+8oQoVKujAgQPy8PCwOZaQkKAuXbooODhY69evd1CFAAAAyGgk9+ZyWHP/448/avfu3akae0ny8PDQ+PHjVaNGDQdUBgAAAGRNDpuWky9fPkVHR9/3eHR0tPLly2e3egAAAGAHFjtsjzGHJfc9evRQly5dNGrUKDVo0EBeXl6SpLi4OG3atEkTJkxQv379HFUeAAAAkOU4rLkfN26ccufOrSlTpmjQoEHW+VeGYcjb21vDhg3T0KFDHVUeAAAATMCce3M5dCnMYcOGadiwYYqKirJZCrNUqVKOLAsAAADIkjLFN9SWKlWKhh4AAOAxQHJvLr6hFgAAAHASmSK5BwAAwOOB5N5cJPcAAACAkyC5BwAAgP0Q3JvK4cn9unXr9MMPP1hfz5kzR5UrV1bHjh11+fJlB1YGAAAAZC0Ob+6HDBmihIQESdLhw4c1aNAgNW3aVFFRUQoJCXFwdQAAAMhIFovF9C09tm/frhYtWsjX11cWi0WrVq2yOd6tW7dU12/cuLHNmEuXLqlTp07y8PBQvnz51L17d129etVmzKFDh1S7dm25u7urePHimjx5cqpaVqxYoSeffFLu7u6qUKGC1q5dm65nkTJBcx8VFaWAgABJ0sqVK9W8eXNNnDhRc+bM0Xfffefg6gAAAODMrl27pkqVKmnOnDn3HdO4cWOdPXvWun3xxRc2xzt16qSjR48qPDxcq1ev1vbt29WrVy/r8YSEBDVq1Eh+fn7at2+fpkyZotDQUH300UfWMTt27FCHDh3UvXt3HThwQK1atVKrVq105MiRdD2Pw+fcu7q66vr165KkjRs3qkuXLpKkAgUKWBN9AAAAOIfMtlpOkyZN1KRJk38c4+bmJm9v73se+/nnn7Vu3Trt2bNH1atXlyS9//77atq0qaZOnSpfX18tXrxYSUlJmj9/vlxdXfXUU08pMjJS06dPt/4SMGvWLDVu3FhDhgyRJI0fP17h4eGaPXu25s2bl+bncXhy//zzzyskJETjx4/X7t271axZM0nSL7/8omLFijm4OmQVtaqW0Zcze+v0hnd148BstahX0eZ4kQJ59dHYzjq94V1d3DFd38x+U2VKFE51nRoVS+m7D/vpwo5pivt+isI/HSB3txzW4ytm9tYva8fp8s4ZOr3hXX06vot8CnvaXKPtC1W0c+nburhjuo6vHaeBXRqY89AAspxPP/5QHV9pq8Bnqqhe7UAN6PemoqNOpxp3MPKAerzWRTWqV1bNZ6vqtS6ddPPmTevxjz+cqy6d2qtGtUp6/rnq9nwEIEtITExUQkKCzZaYmPjQ19u6dauKFCmicuXK6Y033tDFixetxyIiIpQvXz5rYy9JDRs2lIuLi3bt2mUdU6dOHbm6ulrHBAUF6fjx49bPmEZERKhhw4Y29w0KClJERES6anV4cz979mxlz55dX375pebOnauiRYtKkr777rtU85mA+8md002Hf/lDA8KW3fP48hm9VKpYIb084EM912GSYs5e0tp5/ZTL/X//ktWoWErfzH5Tm3YeU+3OU/R85ymat3SbUlIM65jte35R52HzVan1OHUc8olKFy+kJVO6W483qhWgz97tpk++/EHVXn5X/ScuU7/O/1KfdnXMe3gAWcbePbvVrkMnLfpiuT78+DPdvn1bfXp2t/4NtnSnsX+zdw8F1nxei5eu0JJlX6p9x05ycfnff7Jv3bqlFxo11svtOjjiMYBHYo8592FhYfL09LTZwsLCHqrexo0b6z//+Y82bdqkf//739q2bZuaNGmi5ORkSVJsbKyKFClic0727NlVoEABxcbGWsd4eXnZjLn7+kFj7h5PK4dPyylRooRWr16dav+MGTMcUA2yqg0//qQNP/50z2NlSxRRjYqlVLXtBP18+s6/IG9NXKbojRP1SpNqWvD1nd+IJw9qow+WbtXUz8Kt55749ZzNtd5fvMX655izlzX1s3Atn95T2bO76PbtFHVs9qz+u/WgPvnyzgpQ0X9c1JT5GzSo2wuat2x7hj4zgKxn7kef2rwe9+4k1a8dqJ9/Oqpq1Z+RJE35d5g6dHpV3Xv+b75uyVKlbc57s+9bkqRvvv7K5IqBjGePaTnDhw9PtTCLm5vbQ12rffv21j9XqFBBFStWVJkyZbR161Y1aJD5/nbe4cn9/v37dfjwYevrb775Rq1atdI777yjpKQkB1YGZ+Hmeud32JtJt637DMNQUtJt1axcRpJUOH8ePVuxlM5fuqotC0IUvXGiNnzSXzUrl77nNSUpv0cutW9SXTsPRun27RTrvW4m3rYZdyMxScW886uET4GMfjQAWdzVP/+UJHl43pned/HiRR0+dFAFChZUl07tVb9OTb3etbP279vryDKBLMfNzU0eHh4228M2939XunRpFSpUSCdPnpQkeXt769w52zDw9u3bunTpknWevre3t+Li4mzG3H39oDH3m+t/Pw5v7nv37q1ffvlFknT69Gm1b99euXLl0ooVKzR06FAHVwdncDw6VjFnL2l8vxeVL29O5cieTYO6NVQx7/zyLnTnP6ilihWSJI3o3VTzv9qhlsEfKPLn37T2w36p5uZPeKulLuyYpjPbJqu4TwG9PPB/n3QP3/GzWjaopHrPPiGLxaKyJYqof+c7v9X/fW4+gMdbSkqKJv97oipXqSp//yckSX/8/pskad6c2Wrz0sv64MNPVL58gHp176Zff412YLVABrLYYTPR77//rosXL8rHx0eSFBgYqCtXrmjfvn3WMZs3b1ZKSopq1KhhHbN9+3bdunXLOiY8PFzlypVT/vz5rWM2bdpkc6/w8HAFBgamqz6HN/e//PKLKleuLOnO2p516tTRkiVLtGDBAq1cufKB59/rAxNGSrLJVSMruX07Re0HfayyfkV0dvsUXYqYrjrVn9C6H44qxbiTuLu43PlJ8OnKH7To2506ePx3DZ32lX6JPqeuLW3/pZrxn416rv2/1azPbCUnp+iT8a9aj83/6kfNW7pdX83qo4TdM7XtP4O0Yv2df9lTUlLs9MQAsoKJE8bq1IkTmjz1f9NQ7/6ceOmVdmrVuq3Klw/QkLffUclSpbTqqwf/NxFA+l29elWRkZGKjIyUdGeZ9sjISMXExOjq1asaMmSIdu7cqejoaG3atEktW7ZU2bJlFRQUJEkqX768GjdurJ49e2r37t368ccf1bdvX7Vv316+vr6SpI4dO8rV1VXdu3fX0aNHtWzZMs2aNctm6lD//v21bt06TZs2TceOHVNoaKj27t2rvn37put5HD7n3jAM6w+zjRs3qnnz5pKk4sWL68KFCw88PywsTGPHjrXZl83rGeXweTbji0WWdeDn3/Rc+0nyyOMu1xzZdeHyVW3/z2Dt+ylGknT2/J1lV+/Oyb/reFSsinvnt9l38co1XbxyTSdjzul4VKxOrp+gGhVLadehKEnSyPe+0ejZ38q7oIfOX76q+jXKSZKi/rgoAJCkiRPGafu2rZq/8HN5/eWv3AsVvvM3haXLlLEZX6p0GcWePWPXGgGzZLalMPfu3av69etbX99tuLt27aq5c+fq0KFDWrhwoa5cuSJfX181atRI48ePt5nms3jxYvXt21cNGjSQi4uL2rZtq/fee8963NPTUxs2bFBwcLCqVaumQoUKafTo0TZr4desWVNLlizRyJEj9c4778jf31+rVq3S008/na7ncXhzX716dU2YMEENGzbUtm3bNHfuXEl3fmv6+yeG7+VeH5goUnuYKbUi60u4emcpuTIlCqtqQAmN/eDOh7l/PXNRZ85d0RMlbT/tXtavyH0/qCv9L/F3zWH7r1JKiqEz5+MlSa80rqadB0/rwuWrqc4H8HgxDENh747X5k3h+nTBIhUrVtzmeNGixVS4SBFFR0XZ7P81OlrP12bVLcAM9erVk2EY9z2+fv36B16jQIECWrJkyT+OqVixor7//vt/HPPyyy/r5ZdffuD9/onDm/uZM2eqU6dOWrVqlUaMGKGyZctKkr788kvVrFnzgee7ubml+oCExSWbKbUi88qd01Vliv9vbnzJogVV8YmiupxwXb/FXlabhlV0/vJV/RZ7SU/7+2rqkJf0362HtGnnMes5MxZu1Mg+zXT4lz908Pjv6tyihsqV9FLHIXdWt3jmaT9Ve8pPOw6c0pU/r6tUscIa82YznYo5b03tC+bLrdYNq2j73hNyd82uLi2fU5uGVdSoxyz7viEAMqWJ48fqu7WrNfP9D5Q7V25dOH9ekpQnb165u7vLYrGo22vdNXfO+ypX7kmVe7K8vv3ma0VHnda0Gf9LAc+eOaP4+HidPXtGycnJOvbzz5LurECXK3duhzwbkFaZLbl3Nhbjn35VcaCbN28qW7ZsypEjx4MH/03OKumbm4Ssr3Y1f234pH+q/Yu+3aleYz7Xmx3qamCXhipSMK9iLyRo8epdCvtonW7dtv18xuDXXlDvV+oov2cuHf7lD42YuUo7Iu98wcxTZX01dUhbVXiimHLndFXshXht2PGz/v3xOmtKXzBfbq2c1UdPlfWVxSLtOhSl0Nn/1Z4jv5r/JiDTubxntqNLQCZT6aly99w/bkKYWrZuY3396ccfadnSxYqPj1e5ck9qQMhgVa32vy/IGfXO2/r2m69TXeeTz/6jZ56tkfGFI0tzd3iUa6vMoO9Mv8epaf/8jbPOLNM294+C5h5AZkBzDyAzyGzNfdnB5jf3J6c+vs29w/9xJycna8aMGVq+fLliYmJSrW1/6dIlB1UGAAAAZC0OXwpz7Nixmj59utq1a6f4+HiFhISoTZs2cnFxUWhoqKPLAwAAQAayWCymb48zhzf3ixcv1scff6xBgwYpe/bs6tChgz755BONHj1aO3fudHR5AAAAQJbh8OY+NjZWFSpUkCTlyZNH8fF3PpjYvHlzrVmzxpGlAQAAIINZLOZvjzOHN/fFihXT2bNnJUllypTRhg0bJEl79uxJtcQlAAAAgPtzeHPfunVrbdq0SZLUr18/jRo1Sv7+/urSpYtef/11B1cHAACAjMSce3M5fLWcSZMmWf/crl07lShRQhEREfL391eLFi0cWBkAAACQtTi8uf+7wMBABQYGOroMAAAAmOAxD9ZN55Dm/ttvv03z2BdffNHESgAAAADn4ZDmvlWrVmkaZ7FYlJycbG4xAAAAsBsXF6J7MzmkuU9JSXHEbQEAAACnlunm3AMAAMB5MefeXA5bCnPz5s0KCAhQQkJCqmPx8fF66qmntH37dgdUBgAAAGRNDmvuZ86cqZ49e8rDwyPVMU9PT/Xu3VszZsxwQGUAAAAwC+vcm8thzf3BgwfVuHHj+x5v1KiR9u3bZ8eKAAAAgKzNYXPu4+LilCNHjvsez549u86fP2/HigAAAGC2xzxYN53DkvuiRYvqyJEj9z1+6NAh+fj42LEiAAAAIGtzWHPftGlTjRo1Sjdv3kx17MaNGxozZoyaN2/ugMoAAABgFubcm8th03JGjhypr776Sk888YT69u2rcuXKSZKOHTumOXPmKDk5WSNGjHBUeQAAAECW47Dm3svLSzt27NAbb7yh4cOHyzAMSXd+mwsKCtKcOXPk5eXlqPIAAABggsc9WTebQ7/Eys/PT2vXrtXly5d18uRJGYYhf39/5c+f35FlAQAAAFlSpviG2vz58+uZZ55xdBkAAAAwGcG9uRz2gVoAAAAAGStTJPcAAAB4PDDn3lwk9wAAAICTILkHAACA3RDcm4vkHgAAAHASJPcAAACwG+bcm4vkHgAAAHASJPcAAACwG4J7c5HcAwAAAE6C5B4AAAB2w5x7c5HcAwAAAE6C5B4AAAB2Q3BvLpJ7AAAAwEmQ3AMAAMBumHNvLpJ7AAAAwEmQ3AMAAMBuCO7NRXIPAAAAOAmSewAAANgNc+7NRXIPAAAAOAmSewAAANgNwb25SO4BAAAAJ0FyDwAAALthzr25SO4BAAAAJ0FyDwAAALshuDcXyT0AAADgJEjuAQAAYDfMuTcXyT0AAADgJEjuAQAAYDck9+YiuQcAAACcBM09AAAA7MZiMX9Lj+3bt6tFixby9fWVxWLRqlWrrMdu3bqlYcOGqUKFCsqdO7d8fX3VpUsXnTlzxuYaJUuWlMVisdkmTZpkM+bQoUOqXbu23N3dVbx4cU2ePDlVLStWrNCTTz4pd3d3VahQQWvXrk3fw4jmHgAAAI+xa9euqVKlSpozZ06qY9evX9f+/fs1atQo7d+/X1999ZWOHz+uF198MdXYcePG6ezZs9atX79+1mMJCQlq1KiR/Pz8tG/fPk2ZMkWhoaH66KOPrGN27NihDh06qHv37jpw4IBatWqlVq1a6ciRI+l6HubcAwAAwG4y25z7Jk2aqEmTJvc85unpqfDwcJt9s2fP1rPPPquYmBiVKFHCuj9v3rzy9va+53UWL16spKQkzZ8/X66urnrqqacUGRmp6dOnq1evXpKkWbNmqXHjxhoyZIgkafz48QoPD9fs2bM1b968ND8PyT0AAACcSmJiohISEmy2xMTEDLl2fHy8LBaL8uXLZ7N/0qRJKliwoKpUqaIpU6bo9u3b1mMRERGqU6eOXF1drfuCgoJ0/PhxXb582TqmYcOGNtcMCgpSREREuuqjuQcAAIDd2GPOfVhYmDw9PW22sLCwR6795s2bGjZsmDp06CAPDw/r/rfeektLly7Vli1b1Lt3b02cOFFDhw61Ho+NjZWXl5fNte6+jo2N/ccxd4+nFdNyAAAA4FSGDx+ukJAQm31ubm6PdM1bt27plVdekWEYmjt3rs2xv96rYsWKcnV1Ve/evRUWFvbI900vmnsAAADYjT3m3Lu5uWVoU323sf/111+1efNmm9T+XmrUqKHbt28rOjpa5cqVk7e3t+Li4mzG3H19d57+/cbcbx7//TAtBwAAAHaT2ZbCfJC7jf2JEye0ceNGFSxY8IHnREZGysXFRUWKFJEkBQYGavv27bp165Z1THh4uMqVK6f8+fNbx2zatMnmOuHh4QoMDExXvST3AAAAeGxdvXpVJ0+etL6OiopSZGSkChQoIB8fH7300kvav3+/Vq9ereTkZOsc+AIFCsjV1VURERHatWuX6tevr7x58yoiIkIDBw5U586drY17x44dNXbsWHXv3l3Dhg3TkSNHNGvWLM2YMcN63/79+6tu3bqaNm2amjVrpqVLl2rv3r02y2WmhcUwDCMD3pdMJWeVvo4uAQB0ec9sR5cAAHLPZFHuC7N3mn6P8L7PpXns1q1bVb9+/VT7u3btqtDQUJUqVeqe523ZskX16tXT/v379eabb+rYsWNKTExUqVKl9OqrryokJMRmatChQ4cUHBysPXv2qFChQurXr5+GDRtmc80VK1Zo5MiRio6Olr+/vyZPnqymTZum+VkkmnsAMA3NPYDMgOb+8ZLJ/nEDAADAmWWy77ByOnygFgAAAHASJPcAAACwG3sshfk4I7kHAAAAnATJPQAAAOzGheDeVCT3AAAAgJMguQcAAIDdMOfeXCT3AAAAgJMguQcAAIDdENybi+QeAAAAcBIk9wAAALAbi4juzURyDwAAADgJknsAAADYDevcm4vkHgAAAHASJPcAAACwG9a5NxfJPQAAAOAkSO4BAABgNwT35iK5BwAAAJwEyT0AAADsxoXo3lQk9wAAAICTILkHAACA3RDcm4vkHgAAAHASJPcAAACwG9a5NxfJPQAAAOAkSO4BAABgNwT35kpTc3/o0KE0X7BixYoPXQwAAACAh5em5r5y5cqyWCwyDOOex+8es1gsSk5OztACAQAA4DxY595caWruo6KizK4DAAAAwCNKU3Pv5+dndh0AAAB4DJDbm+uhVstZtGiRatWqJV9fX/3666+SpJkzZ+qbb77J0OIAAAAApF26m/u5c+cqJCRETZs21ZUrV6xz7PPly6eZM2dmdH0AAABwIhaLxfTtcZbu5v7999/Xxx9/rBEjRihbtmzW/dWrV9fhw4cztDgAAAAAaZfude6joqJUpUqVVPvd3Nx07dq1DCkKAAAAzsnl8Q7WTZfu5L5UqVKKjIxMtX/dunUqX758RtQEAAAA4CGkO7kPCQlRcHCwbt68KcMwtHv3bn3xxRcKCwvTJ598YkaNAAAAcBKP+5x4s6W7ue/Ro4dy5sypkSNH6vr16+rYsaN8fX01a9YstW/f3owaAQAAAKRBupt7SerUqZM6deqk69ev6+rVqypSpEhG1wUAAAAnRHBvrodq7iXp3LlzOn78uKQ7f71SuHDhDCsKAAAAQPql+wO1f/75p1599VX5+vqqbt26qlu3rnx9fdW5c2fFx8ebUSMAAACcBOvcmyvdzX2PHj20a9curVmzRleuXNGVK1e0evVq7d27V7179zajRgAAAABpkO5pOatXr9b69ev1/PPPW/cFBQXp448/VuPGjTO0OAAAADgX1rk3V7qT+4IFC8rT0zPVfk9PT+XPnz9DigIAAACQfulu7keOHKmQkBDFxsZa98XGxmrIkCEaNWpUhhYHAAAA58Kce3OlaVpOlSpVbN6oEydOqESJEipRooQkKSYmRm5ubjp//jzz7gEAAAAHSVNz36pVK5PLAAAAwOPg8c7VzZem5n7MmDFm1wEAAADgET30l1gBAAAA6eXymM+JN1u6m/vk5GTNmDFDy5cvV0xMjJKSkmyOX7p0KcOKAwAAAJB26V4tZ+zYsZo+fbratWun+Ph4hYSEqE2bNnJxcVFoaKgJJQIAAMBZWCzmb4+zdDf3ixcv1scff6xBgwYpe/bs6tChgz755BONHj1aO3fuNKNGAAAAAGmQ7uY+NjZWFSpUkCTlyZNH8fHxkqTmzZtrzZo1GVsdAAAAnArr3Jsr3c19sWLFdPbsWUlSmTJltGHDBknSnj175ObmlrHVAQAAAEizdDf3rVu31qZNmyRJ/fr106hRo+Tv768uXbro9ddfz/ACAQAA4DyYc2+udK+WM2nSJOuf27VrJz8/P+3YsUP+/v5q0aJFhhYHAAAAIO3Sndz/3XPPPaeQkBDVqFFDEydOzIiaAAAA4KRcLBbTt/TYvn27WrRoIV9fX1ksFq1atcrmuGEYGj16tHx8fJQzZ041bNhQJ06csBlz6dIlderUSR4eHsqXL5+6d++uq1ev2ow5dOiQateuLXd3dxUvXlyTJ09OVcuKFSv05JNPyt3dXRUqVNDatWvT9SxSBjT3d509e1ajRo3KqMsBAAAAprt27ZoqVaqkOXPm3PP45MmT9d5772nevHnatWuXcufOraCgIN28edM6plOnTjp69KjCw8O1evVqbd++Xb169bIeT0hIUKNGjeTn56d9+/ZpypQpCg0N1UcffWQds2PHDnXo0EHdu3fXgQMH1KpVK7Vq1UpHjhxJ1/NYDMMw0vke3NPBgwdVtWpVJScnZ8TlHknOKn0dXQIA6PKe2Y4uAQDknu5J2OZ686ufTL/HB20CHuo8i8Wir7/+Wq1atZJ0J7X39fXVoEGDNHjwYElSfHy8vLy8tGDBArVv314///yzAgICtGfPHlWvXl2StG7dOjVt2lS///67fH19NXfuXI0YMUKxsbFydXWVJL399ttatWqVjh07JunOdPdr165p9erV1nqee+45Va5cWfPmzUvzM2RYcg8AAABkBomJiUpISLDZEhMT032dqKgoxcbGqmHDhtZ9np6eqlGjhiIiIiRJERERypcvn7Wxl6SGDRvKxcVFu3btso6pU6eOtbGXpKCgIB0/flyXL1+2jvnrfe6OuXuftKK5BwAAgN3YY537sLAweXp62mxhYWHprjU2NlaS5OXlZbPfy8vLeiw2NlZFihSxOZ49e3YVKFDAZsy9rvHXe9xvzN3jaZXmv6gJCQn5x+Pnz59P140BAAAAMwwfPjxV7/q4fB9Tmpv7AwcOPHBMnTp1HqmYjMI8VwCZQf5n+PwPAMe7cSBz9UX2mDbi5uaWIc28t7e3JCkuLk4+Pj7W/XFxcapcubJ1zLlz52zOu337ti5dumQ939vbW3FxcTZj7r5+0Ji7x9Mqzc39li1b0nVhAAAA4O8sWehbpkqVKiVvb29t2rTJ2swnJCRo165deuONNyRJgYGBunLlivbt26dq1apJkjZv3qyUlBTVqFHDOmbEiBG6deuWcuTIIUkKDw9XuXLllD9/fuuYTZs2acCAAdb7h4eHKzAwMF01M+ceAAAAj62rV68qMjJSkZGRku58iDYyMlIxMTGyWCwaMGCAJkyYoG+//VaHDx9Wly5d5Ovra11Rp3z58mrcuLF69uyp3bt368cff1Tfvn3Vvn17+fr6SpI6duwoV1dXde/eXUePHtWyZcs0a9Ysm6lD/fv317p16zRt2jQdO3ZMoaGh2rt3r/r2Td/fAmeyxZEAAADgzFwyWXC/d+9e1a9f3/r6bsPdtWtXLViwQEOHDtW1a9fUq1cvXblyRc8//7zWrVsnd3d36zmLFy9W37591aBBA7m4uKht27Z67733rMc9PT21YcMGBQcHq1q1aipUqJBGjx5tsxZ+zZo1tWTJEo0cOVLvvPOO/P39tWrVKj399NPpep4MW+c+M7l529EVAABz7gFkDpltzv2Ab46Zfo+ZLZ80/R6ZFck9AAAA7CazJffOhjn3AAAAgJN4qOb++++/V+fOnRUYGKg//vhDkrRo0SL98MMPGVocAAAAnIs9vsTqcZbu5n7lypUKCgpSzpw5deDAAetX+cbHx2vixIkZXiAAAACAtEl3cz9hwgTNmzdPH3/8sXWdTkmqVauW9u/fn6HFAQAAwLm4WMzfHmfpbu6PHz9+z2+i9fT01JUrVzKiJgAAAAAPId3Nvbe3t06ePJlq/w8//KDSpUtnSFEAAABwThaL+dvjLN3Nfc+ePdW/f3/t2rVLFotFZ86c0eLFizV48GDr1/ACAAAAsL90r3P/9ttvKyUlRQ0aNND169dVp04dubm5afDgwerXr58ZNQIAAMBJuDzu0brJ0t3cWywWjRgxQkOGDNHJkyd19epVBQQEKE+ePGbUBwAAACCNHvobal1dXRUQEJCRtQAAAMDJ8Q2q5kp3c1+/fv1//HKAzZs3P1JBAAAAAB5Oupv7ypUr27y+deuWIiMjdeTIEXXt2jWj6gIAAIATYsq9udLd3M+YMeOe+0NDQ3X16tVHLggAAADAw8mwaU+dO3fW/PnzM+pyAAAAcEIuFovp2+Msw5r7iIgIubu7Z9TlAAAAAKRTuqfltGnTxua1YRg6e/as9u7dq1GjRmVYYQAAAHA+j3mwbrp0N/eenp42r11cXFSuXDmNGzdOjRo1yrDCAAAAAKRPupr75ORkvfbaa6pQoYLy589vVk0AAABwUi4k96ZK15z7bNmyqVGjRrpy5YpJ5QAAAAB4WOn+QO3TTz+t06dPm1ELAAAAnByr5Zgr3c39hAkTNHjwYK1evVpnz55VQkKCzQYAAADAMdI8537cuHEaNGiQmjZtKkl68cUXZfnLb0aGYchisSg5OTnjqwQAAIBTeMyDddOlubkfO3as+vTpoy1btphZDwAAAICHlObm3jAMSVLdunVNKwYAAADOjdVyzJWuOfcW/h4FAAAAyLTStc79E0888cAG/9KlS49UEAAAAJyXRYTFZkpXcz927NhU31ALAAAAIHNIV3Pfvn17FSlSxKxaAAAA4OSYc2+uNM+5Z749AAAAkLmle7UcAAAA4GGR3Jsrzc19SkqKmXUAAAAAeETpmnMPAAAAPAqmepsrXevcAwAAAMi8SO4BAABgN8y5NxfJPQAAAOAkSO4BAABgN0y5NxfJPQAAAOAkSO4BAABgNy5E96YiuQcAAACcBMk9AAAA7IbVcsxFcg8AAAA4CZJ7AAAA2A1T7s1Fcg8AAAA4CZJ7AAAA2I2LiO7NRHIPAAAAOAmSewAAANgNc+7NRXIPAAAAOAmSewAAANgN69ybi+QeAAAAcBIk9wAAALAbFybdm4rkHgAAAHASJPcAAACwG4J7c5HcAwAAAE6C5h4AAAB242KxmL6lR8mSJWWxWFJtwcHBkqR69eqlOtanTx+ba8TExKhZs2bKlSuXihQpoiFDhuj27ds2Y7Zu3aqqVavKzc1NZcuW1YIFCx7pfbwfpuUAAADgsbVnzx4lJydbXx85ckQvvPCCXn75Zeu+nj17aty4cdbXuXLlsv45OTlZzZo1k7e3t3bs2KGzZ8+qS5cuypEjhyZOnChJioqKUrNmzdSnTx8tXrxYmzZtUo8ePeTj46OgoKAMfR6aewAAANhNZptzX7hwYZvXkyZNUpkyZVS3bl3rvly5csnb2/ue52/YsEE//fSTNm7cKC8vL1WuXFnjx4/XsGHDFBoaKldXV82bN0+lSpXStGnTJEnly5fXDz/8oBkzZmR4c8+0HAAAADiVxMREJSQk2GyJiYkPPC8pKUmff/65Xn/9dVn+8lvI4sWLVahQIT399NMaPny4rl+/bj0WERGhChUqyMvLy7ovKChICQkJOnr0qHVMw4YNbe4VFBSkiIiIR33UVGjuAQAAYDcudtjCwsLk6elps4WFhT2wtlWrVunKlSvq1q2bdV/Hjh31+eefa8uWLRo+fLgWLVqkzp07W4/HxsbaNPaSrK9jY2P/cUxCQoJu3LjxwLrSg2k5AAAAcCrDhw9XSEiIzT43N7cHnvfpp5+qSZMm8vX1te7r1auX9c8VKlSQj4+PGjRooFOnTqlMmTIZV3QGobkHAACA3VjsMOnezc0tTc38X/3666/auHGjvvrqq38cV6NGDUnSyZMnVaZMGXl7e2v37t02Y+Li4iTJOk/f29vbuu+vYzw8PJQzZ8501fkgTMsBAADAY++zzz5TkSJF1KxZs38cFxkZKUny8fGRJAUGBurw4cM6d+6cdUx4eLg8PDwUEBBgHbNp0yab64SHhyswMDADn+AOmnsAAADYjcUOW3qlpKTos88+U9euXZU9+/8mtpw6dUrjx4/Xvn37FB0drW+//VZdunRRnTp1VLFiRUlSo0aNFBAQoFdffVUHDx7U+vXrNXLkSAUHB1v/9qBPnz46ffq0hg4dqmPHjumDDz7Q8uXLNXDgwIeo9p8xLQcAAAB2k94vmbKHjRs3KiYmRq+//rrNfldXV23cuFEzZ87UtWvXVLx4cbVt21YjR460jsmWLZtWr16tN954Q4GBgcqdO7e6du1qsy5+qVKltGbNGg0cOFCzZs1SsWLF9Mknn2T4MpiSZDEMw8jwqzrYzdsPHgMAZsv/TF9HlwAAunFgtqNLsPH5vt9Nv0fnasVMv0dmRXIPAAAAu8l8ub1zYc49AAAA4CRI7gEAAGA3mXDKvVMhuQcAAACcBMk9AAAA7MYeX2L1OCO5BwAAAJwEyT0AAADshmTZXLy/AAAAgJMguQcAAIDdMOfeXCT3AAAAgJMguQcAAIDdkNubi+QeAAAAcBIk9wAAALAb5tybi+QeAAAAcBIk9wAAALAbkmVz8f4CAAAAToLkHgAAAHbDnHtzkdwDAAAAToLkHgAAAHZDbm8uknsAAADASZDcAwAAwG6Ycm8uknsAAADASZDcAwAAwG5cmHVvKpJ7AAAAwEmQ3AMAAMBumHNvLpJ7AAAAwEmQ3AMAAMBuLMy5NxXJPQAAAOAkSO4BAABgN8y5NxfJPQAAAOAkSO4BAABgN6xzby6SewAAAMBJkNwDAADAbphzb65Mm9zHxcVp3Lhxji4DAAAAyDIybXMfGxursWPHOroMAAAAZCCLxfztceawaTmHDh36x+PHjx+3UyUAAACAc3BYc1+5cmVZLBYZhpHq2N39lsf9Vy8AAAAnwzfUmsthzX2BAgU0efJkNWjQ4J7Hjx49qhYtWti5KgAAACDrclhzX61aNZ05c0Z+fn73PH7lypV7pvoAAADIulwI7k3lsOa+T58+unbt2n2PlyhRQp999pkdKwIAAACyNoc1961bt/7H4/nz51fXrl3tVA0AAADsgTn35sq0S2ECAAAASB++oRYAAAB2w2KI5iK5BwAAAJwEyT0AAADshjn35iK5BwAAAJyEw5v7devW6YcffrC+njNnjipXrqyOHTvq8uXLDqwMAAAAGc3FYv72OHN4cz9kyBAlJCRIkg4fPqxBgwapadOmioqKUkhIiIOrAwAAALIOh8+5j4qKUkBAgCRp5cqVat68uSZOnKj9+/eradOmDq4OAAAAGYk59+ZyeHLv6uqq69evS5I2btyoRo0aSZIKFChgTfQBAAAAPJjDk/vnn39eISEhqlWrlnbv3q1ly5ZJkn755RcVK1bMwdXBWSxfukTLl32hM3/8IUkqU9Zfvd94U8/XritJ+nL5Mn23drV+/umorl27pu8j9sjDw+Oe10pKSlLn9i/r+PFjWvblKj1ZvrzdngNA5larahkN7NJQVQNKyKewp14Z+JH+u/WQ9XiRAnk1oX9LNQwsL888OfXD/pMKmbxCp2LOW8es/7i/6lT3t7nux1/+oLfeXWp9XS2ghMa/1VJVAorLMKS9R37ViFmrdPiXP2zOG/BqA73etpZK+OTXxSvX9OHy7zX50/UmPT2QNqxzby6HJ/ezZ89W9uzZ9eWXX2ru3LkqWrSoJOm7775T48aNHVwdnEURL2/1HzhYX6z4SkuWr9SzNZ5T/77BOnnyhCTp5s0bqlmrtrr37PPAa82YNlmFixQxu2QAWVDunG46/MsfGhC27J7Hl8/opVLFCunlAR/quQ6TFHP2ktbO66dc7q424z5d+aNKNhxu3UbMXPWXe7jqmznB+i32suq8OlUNXpuuq9dv6ts5wcqe/X//WZ829CV1ax2o4TO+VqXWE/TSgA+198ivpjw3gMzD4cl9iRIltHr16lT7Z8yY4YBq4Kzq1f+Xzet+/Qdq+dIvdOhgpMqW9VfnLt0kSXt27/rH6/zw/TZF7PhR02a8rx++325WuQCyqA0//qQNP/50z2NlSxRRjYqlVLXtBP18OlaS9NbEZYreOFGvNKmmBV9HWMfeuJmkuIt/3vM65Up5q2C+3Bo/d7V+j7siSXr3w++0d8U7KuFTQKd/u6BypbzU86Xaqvbyuzrx6zlJ0q9nLmbgkwIPj+DeXA5P7vfv36/Dhw9bX3/zzTdq1aqV3nnnHSUlJTmwMjir5ORkfbd2jW7cuK5Klaqk+byLFy5o7JhRejdsstxzuptYIQBn5OZ6J0+7mXTbus8wDCUl3VbNymVsxrZrWl2/bZ6kvSve0bh+Lyqnew7rsV+i43Th8lV1bVVTObJnk7tbDnVrFaifT5/Vr2cuSZKa1amgqD8uqGmdp/Xz6lAdWzNWH4zuqPweuezwpAAcyeHNfe/evfXLL79Ikk6fPq327dsrV65cWrFihYYOHerg6uBMTvxyXM9Vr6JnqlTQu+PGaMZ7c1SmbNk0nWsYhkaNeFsvv9JeTz1dweRKATij49Gxijl7SeP7vah8eXMqR/ZsGtStoYp555d3IU/ruGXf7dXrI/6jxr3e09T5G9Sx2TP6bEJX6/Gr1xMV1HOWOjR9Rpd3ztCFH6fphZrl1arvB0pOTpEklSxWSCV8CqhNwyrqMWqReo7+XFXKF9eSKd3t/tzA37lYLKZv6REaGiqLxWKzPfnkk9bjN2/eVHBwsAoWLKg8efKobdu2iouLs7lGTEyMmjVrply5cqlIkSIaMmSIbt++bTNm69atqlq1qtzc3FS2bFktWLDgod/Df+Lw5v6XX35R5cqVJUkrVqxQnTp1tGTJEi1YsEArV6584PmJiYlKSEiw2RITE02uGllRyZKltHzlKn3+xXK93K6DRr0zTKdOnkzTuUsWL9K1a9fUvWdvk6sE4Kxu305R+0Efq6xfEZ3dPkWXIqarTvUntO6Ho0oxUqzj5n/1ozZG/KyjJ89o6Xd71X3UIrVsUFmlihWSJLm75dC8MZ0UcfC06naZqn+9Nl0/nTqrr957Q+5udxJ+F4tF7m451H3UIv144JS+33dCb4xdrHrPlpO/H58ZAv7uqaee0tmzZ63bX79gdeDAgfrvf/+rFStWaNu2bTpz5ozatGljPZ6cnKxmzZopKSlJO3bs0MKFC7VgwQKNHj3aOiYqKkrNmjVT/fr1FRkZqQEDBqhHjx5avz7jP+Du8ObeMAylpNz5obZx40br2vbFixfXhQsXHnh+WFiYPD09bbYp/w4ztWZkTTlcXVXCz08BTz2t/gMH6YlyT2rx5/9J07l7du3UoYOReqZKBVWtGKAWTe4s2dqxXVuNHD7MzLIBOJEDP/+m59pPklftwSrVaIRa9v1ABT1zK+r3+8+H33M4WpJUpnhhSVK7JtVVwreAeo35XPt+itHuw9HqOnyBShYtqBb1KkqSYi/E69atZJ2MOWe9zrGoO0ljce8CJj0dkDYWO2zplT17dnl7e1u3QoXu/DIdHx+vTz/9VNOnT9e//vUvVatWTZ999pl27NihnTt3SpI2bNign376SZ9//rkqV66sJk2aaPz48ZozZ451ivm8efNUqlQpTZs2TeXLl1ffvn310ksvmfIZU4c399WrV9eECRO0aNEibdu2Tc2aNZN05zccLy+vB54/fPhwxcfH22xDhg03u2w4gZSUFN1K4+c6hg0fqeVffaNlK1dp2cpVmj33I0nS5Kkz1K//QDPLBOCEEq7e1IXLV1WmRGFVDSih1X9ZLvPvKpW7syx07IV4SVIud1elpBgyDMM6JsUwZBiyTkeIiDytHDmyWdN+SdbEPubspQx/HiCzSe/MjhMnTsjX11elS5dWp06dFBMTI0nat2+fbt26pYYNG1rHPvnkkypRooQiIu58CD4iIkIVKlSw6VuDgoKUkJCgo0ePWsf89Rp3x9y9RkZy+Go5M2fOVKdOnbRq1SqNGDFCZf9/DvSXX36pmjVrPvB8Nzc3ubm52ey7efs+g/HYmjVjmp6vXUfePj66fu2a1q5Zrb17dmvuR59Kki6cP68LFy7ot///l/nkiV+UK1du+fj4yDNfPvn4+tpcL1euOx9KK1a8hLy8ve37MAAyrdw5Xa0JuySVLFpQFZ8oqssJ1/Vb7GW1aVhF5y9f1W+xl/S0v6+mDnlJ/916SJt2HpMklSpWSO2aVNf6H47q4pVrqvBEUU0e1Ebf7zuhIyfOSJI27TymiQNaaebwVzR36Ta5WCwa/Foj3U5O1ra9dz7DtnnXce3/KUYfhnbSkCkr5eJi0cy3X9HGiJ9t0nzAIeywXE5YWJjGjh1rs2/MmDEKDQ1NNbZGjRpasGCBypUrp7Nnz2rs2LGqXbu2jhw5otjYWLm6uipfvnw253h5eSk29s6qV7GxsakC6buvHzQmISFBN27cUM6cOR/lcW04vLmvWLGizWo5d02ZMkXZsmVzQEVwRpcuXdTI4cN0/vw55cmbV088UU5zP/pUgTVrSZJWLF+qeR/Mto5/rUsnSdK4CWFq2brNPa8JAH9XNcBPGz7pb309eXBbSdKib3eq15jP5V3YQ/8e1EZFCuZV7IUELV69S2EfrbOOv3Xrtv5Vo5z6dqyv3Dld9XvcZa3aFKlJn/xvXu4v0XFq2/9DjejdRFsXDlJKiqGDx35Xy+APFHvhzje7G4ahlwZ8qOnDXlb4pwN07UaSNvz4k96e/pWd3gnAsYYPH66QkBCbfX8Pg+9q0qSJ9c8VK1ZUjRo15Ofnp+XLl2do020vDm/u78fdnaUGkXHGjp/4j8ffCO6nN4L7pfl6RYsW08Gjxx+1LABO5vt9J5SzSt/7Hv/gi2364Itt9z3+e9wVNeox64H32bzrmDbvOvaPY86ej1eHwZ888FqAvVnsEN3fa2ZHWuXLl09PPPGETp48qRdeeEFJSUm6cuWKTXofFxcn7///m3tvb2/t3r3b5hp3V9P565i/r7ATFxcnDw+PDP8FwuFz7pOTkzV16lQ9++yz8vb2VoECBWw2AAAAwF6uXr2qU6dOycfHR9WqVVOOHDm0adMm6/Hjx48rJiZGgYGBkqTAwEAdPnxY5879b8pbeHi4PDw8FBAQYB3z12vcHXP3GhnJ4c392LFjNX36dLVr107x8fEKCQlRmzZt5OLics95UQAAAMi6LBbzt/QYPHiwtm3bpujoaO3YsUOtW7dWtmzZ1KFDB3l6eqp79+4KCQnRli1btG/fPr322msKDAzUc889J0lq1KiRAgIC9Oqrr+rgwYNav369Ro4cqeDgYOvfHvTp00enT5/W0KFDdezYMX3wwQdavny5Bg7M+EU5HD4tZ/Hixfr444/VrFkzhYaGqkOHDipTpowqVqyonTt36q233nJ0iQAAAHBSv//+uzp06KCLFy+qcOHCev7557Vz504VLnznw/EzZsyQi4uL2rZtq8TERAUFBemDDz6wnp8tWzatXr1ab7zxhgIDA5U7d2517dpV48aNs44pVaqU1qxZo4EDB2rWrFkqVqyYPvnkEwUFBWX481iMv66l5QC5c+fWzz//rBIlSsjHx0dr1qxR1apVdfr0aVWpUkXx8fHpviar5QDIDPI/c/+51wBgLzcOzH7wIDvaczr9vV16PVPa88GDnJTDp+UUK1ZMZ8+elSSVKVNGGzZskCTt2bPnoT8IAQAAgEwqM36LlRNxeHPfunVr6wcM+vXrp1GjRsnf319dunTR66+/7uDqAAAAgKzD4XPuJ02aZP1zu3btrN/45e/vrxYtWjiwMgAAAGQ0eyyF+ThzeHP/d4GBgaYsCwQAAAA4O4c0999++22ax7744osmVgIAAAB7Su9SlUgfhzT3rVq1StM4i8Wi5ORkc4sBAAAAnIRDmvuUlBRH3BYAAAAORnBvLoevlgMAAAAgYzisud+8ebMCAgKUkJCQ6lh8fLyeeuopbd++3QGVAQAAwDSsc28qhzX3M2fOVM+ePeXh4ZHqmKenp3r37q0ZM2Y4oDIAAAAga3JYc3/w4EE1btz4vscbNWqkffv22bEiAAAAmM1ih/89zhzW3MfFxSlHjhz3PZ49e3adP3/ejhUBAAAAWZvDmvuiRYvqyJEj9z1+6NAh+fj42LEiAAAAmM1iMX97nDmsuW/atKlGjRqlmzdvpjp248YNjRkzRs2bN3dAZQAAAEDWZDEMw3DEjePi4lS1alVly5ZNffv2Vbly5SRJx44d05w5c5ScnKz9+/fLy8sr3de+eTujqwWA9Mv/TF9HlwAAunFgtqNLsHEw5k/T71GpRF7T75FZOeRLrCTJy8tLO3bs0BtvvKHhw4fr7u8YFotFQUFBmjNnzkM19gAAAMDjymHNvST5+flp7dq1unz5sk6ePCnDMOTv76/8+fM7siwAAACY5TGfE282hzb3d+XPn1/PPPOMo8sAAAAAsrRM0dwDAADg8fC4r0NvNoetlgMAAAAgY5HcAwAAwG4e93XozUZyDwAAADgJknsAAADYDcG9uUjuAQAAACdBcg8AAAD7Ibo3Fck9AAAA4CRI7gEAAGA3rHNvLpJ7AAAAwEmQ3AMAAMBuWOfeXCT3AAAAgJMguQcAAIDdENybi+QeAAAAcBIk9wAAALAfontTkdwDAAAAToLkHgAAAHbDOvfmIrkHAAAAnATJPQAAAOyGde7NRXIPAAAAOAmSewAAANgNwb25SO4BAAAAJ0FyDwAAAPshujcVyT0AAADgJEjuAQAAYDesc28uknsAAADASZDcAwAAwG5Y595cJPcAAACAkyC5BwAAgN0Q3JuL5B4AAABwEiT3AAAAsB+ie1OR3AMAAABOguQeAAAAdsM69+YiuQcAAACcBMk9AAAA7IZ17s1Fcg8AAAA4CZp7AAAA2I3FDlt6hIWF6ZlnnlHevHlVpEgRtWrVSsePH7cZU69ePVksFputT58+NmNiYmLUrFkz5cqVS0WKFNGQIUN0+/ZtmzFbt25V1apV5ebmprJly2rBggXprPbBaO4BAADw2Nq2bZuCg4O1c+dOhYeH69atW2rUqJGuXbtmM65nz546e/asdZs8ebL1WHJyspo1a6akpCTt2LFDCxcu1IIFCzR69GjrmKioKDVr1kz169dXZGSkBgwYoB49emj9+vUZ+jwWwzCMDL1iJnDz9oPHAIDZ8j/T19ElAIBuHJjt6BJsRF+8afo9ShZ0f+hzz58/ryJFimjbtm2qU6eOpDvJfeXKlTVz5sx7nvPdd9+pefPmOnPmjLy8vCRJ8+bN07Bhw3T+/Hm5urpq2LBhWrNmjY4cOWI9r3379rpy5YrWrVv30PX+Hck9AAAAnEpiYqISEhJstsTExDSdGx8fL0kqUKCAzf7FixerUKFCevrppzV8+HBdv37deiwiIkIVKlSwNvaSFBQUpISEBB09etQ6pmHDhjbXDAoKUkRExEM94/3Q3AMAAMBuLHb4X1hYmDw9PW22sLCwB9aWkpKiAQMGqFatWnr66aet+zt27KjPP/9cW7Zs0fDhw7Vo0SJ17tzZejw2NtamsZdkfR0bG/uPYxISEnTjxo2Hfj//jqUwAQAA4FSGDx+ukJAQm31ubm4PPC84OFhHjhzRDz/8YLO/V69e1j9XqFBBPj4+atCggU6dOqUyZcpkTNEZhOYeAAAAdmOPde7d3NzS1Mz/Vd++fbV69Wpt375dxYoV+8exNWrUkCSdPHlSZcqUkbe3t3bv3m0zJi4uTpLk7e1t/f+7+/46xsPDQzlz5kxXrf+EaTkAAAB4bBmGob59++rrr7/W5s2bVapUqQeeExkZKUny8fGRJAUGBurw4cM6d+6cdUx4eLg8PDwUEBBgHbNp0yab64SHhyswMDCDnuQOmnsAAADYTWZb5z44OFiff/65lixZorx58yo2NlaxsbHWefCnTp3S+PHjtW/fPkVHR+vbb79Vly5dVKdOHVWsWFGS1KhRIwUEBOjVV1/VwYMHtX79eo0cOVLBwcHWv0Ho06ePTp8+raFDh+rYsWP64IMPtHz5cg0cOPBh3sb7YilMADAJS2ECyAwy21KYv11K26o1j6J4gbRPybHcZ57QZ599pm7duum3335T586ddeTIEV27dk3FixdX69atNXLkSHl4eFjH//rrr3rjjTe0detW5c6dW127dtWkSZOUPfv/ZsFv3bpVAwcO1E8//aRixYpp1KhR6tat20M/5z2fh+YeAMxBcw8gM8hszf3vl81v7ovlT998e2fCB2oBAABgR3b4RO1jjDn3AAAAgJMguQcAAIDd2GMpzMcZyT0AAADgJEjuAQAAYDcE9+YiuQcAAACcBMk9AAAA7IY59+YiuQcAAACcBMk9AAAA7MbCrHtTkdwDAAAAToLkHgAAAPZDcG8qknsAAADASZDcAwAAwG4I7s1Fcg8AAAA4CZJ7AAAA2A3r3JuL5B4AAABwEiT3AAAAsBvWuTcXyT0AAADgJEjuAQAAYD8E96YiuQcAAACcBMk9AAAA7Ibg3lwk9wAAAICTILkHAACA3bDOvblI7gEAAAAnQXIPAAAAu2Gde3OR3AMAAABOguQeAAAAdsOce3OR3AMAAABOguYeAAAAcBI09wAAAICTYM49AAAA7IY59+YiuQcAAACcBMk9AAAA7IZ17s1Fcg8AAAA4CZJ7AAAA2A1z7s1Fcg8AAAA4CZJ7AAAA2A3BvblI7gEAAAAnQXIPAAAA+yG6NxXJPQAAAOAkSO4BAABgN6xzby6SewAAAMBJkNwDAADAbljn3lwk9wAAAICTILkHAACA3RDcm4vkHgAAAHASJPcAAACwH6J7U5HcAwAAAE6C5B4AAAB2wzr35iK5BwAAAJwEyT0AAADshnXuzUVyDwAAADgJi2EYhqOLADKbxMREhYWFafjw4XJzc3N0OQAeQ/wcAvAwaO6Be0hISJCnp6fi4+Pl4eHh6HIAPIb4OQTgYTAtBwAAAHASNPcAAACAk6C5BwAAAJwEzT1wD25ubhozZgwfYgPgMPwcAvAw+EAtAAAA4CRI7gEAAAAnQXMPAAAAOAmaezg9i8WiVatWOboMAI8xfg4BsBeae2RpsbGx6tevn0qXLi03NzcVL15cLVq00KZNmxxdmiTJMAyNHj1aPj4+ypkzpxo2bKgTJ044uiwAGSiz/xz66quv1KhRIxUsWFAWi0WRkZGOLgmAiWjukWVFR0erWrVq2rx5s6ZMmaLDhw9r3bp1ql+/voKDgx1dniRp8uTJeu+99zRv3jzt2rVLuXPnVlBQkG7evOno0gBkgKzwc+jatWt6/vnn9e9//9vRpQCwBwPIopo0aWIULVrUuHr1aqpjly9ftv5ZkvH1119bXw8dOtTw9/c3cubMaZQqVcoYOXKkkZSUZD0eGRlp1KtXz8iTJ4+RN29eo2rVqsaePXsMwzCM6Ohoo3nz5ka+fPmMXLlyGQEBAcaaNWvuWV9KSorh7e1tTJkyxbrvypUrhpubm/HFF1884tMDyAwy+8+hv4qKijIkGQcOHHjo5wWQ+WV38O8WwEO5dOmS1q1bp3fffVe5c+dOdTxfvnz3PTdv3rxasGCBfH19dfjwYfXs2VN58+bV0KFDJUmdOnVSlSpVNHfuXGXLlk2RkZHKkSOHJCk4OFhJSUnavn27cufOrZ9++kl58uS5532ioqIUGxurhg0bWvd5enqqRo0aioiIUPv27R/hHQDgaFnh5xCAxw/NPbKkkydPyjAMPfnkk+k+d+TIkdY/lyxZUoMHD9bSpUut/1GNiYnRkCFDrNf29/e3jo+JiVHbtm1VoUIFSVLp0qXve5/Y2FhJkpeXl81+Ly8v6zEAWVdW+DkE4PHDnHtkScYjfPfasmXLVKtWLXl7eytPnjwaOXKkYmJirMdDQkLUo0cPNWzYUJMmTdKpU6esx9566y1NmDBBtWrV0pgxY3To0KFHeg4AWRc/hwBkRjT3yJL8/f1lsVh07NixdJ0XERGhTp06qWnTplq9erUOHDigESNGKCkpyTomNDRUR48eVbNmzbR582YFBATo66+/liT16NFDp0+f1quvvqrDhw+revXqev/99+95L29vb0lSXFyczf64uDjrMQBZV1b4OQTgMeTYKf/Aw2vcuHG6P8g2depUo3Tp0jZju3fvbnh6et73Pu3btzdatGhxz2Nvv/22UaFChXseu/uB2qlTp1r3xcfH84FawIlk9p9Df8UHaoHHA8k9sqw5c+YoOTlZzz77rFauXKkTJ07o559/1nvvvafAwMB7nuPv76+YmBgtXbpUp06d0nvvvWdNwyTpxo0b6tu3r7Zu3apff/1VP/74o/bs2aPy5ctLkgYMGKD169crKipK+/fv15YtW6zH/s5isWjAgAGaMGGCvv32Wx0+fFhdunSRr6+vWrVqleHvBwD7y+w/h6Q7H/yNjIzUTz/9JEk6fvy4IiMj+ewP4Kwc/dsF8CjOnDljBAcHG35+foarq6tRtGhR48UXXzS2bNliHaO/LUE3ZMgQo2DBgkaePHmMdu3aGTNmzLAmZomJiUb79u2N4sWLG66uroavr6/Rt29f48aNG4ZhGEbfvn2NMmXKGG5ubkbhwoWNV1991bhw4cJ960tJSTFGjRpleHl5GW5ubkaDBg2M48ePm/FWAHCQzP5z6LPPPjMkpdrGjBljwrsBwNEshvEInwgCAAAAkGkwLQcAAABwEjT3AAAAgJOguQcAAACcBM09AAAA4CRo7gEAAAAnQXMPAAAAOAmaewAAAMBJ0NwDAAAAToLmHsBjp1u3bmrVqpX1db169TRgwAC717F161ZZLBZduXLFtHv8/Vkfhj3qBABkDJp7AJlCt27dZLFYZLFY5OrqqrJly2rcuHG6ffu26ff+6quvNH78+DSNtXejW7JkSc2cOdMu9wIAZH3ZHV0AANzVuHFjffbZZ0pMTNTatWsVHBysHDlyaPjw4anGJiUlydXVNUPuW6BAgQy5DgAAjkZyDyDTcHNzk7e3t/z8/PTGG2+oYcOG+vbbbyX9b3rJu+++K19fX5UrV06S9Ntvv+mVV15Rvnz5VKBAAbVs2VLR0dHWayYnJyskJET58uVTwYIFNXToUBmGYXPfv0/LSUxM1LBhw1S8eHG5ubmpbNmy+vTTTxUdHa369etLkvLnzy+LxaJu3bpJklJSUhQWFqZSpUopZ86cqlSpkr788kub+6xdu1ZPPPGEcubMqfr169vU+TCSk5PVvXt36z3LlSunWbNm3XPs2LFjVbhwYXl4eKhPnz5KSkqyHktL7QCArIHkHkCmlTNnTl28eNH6etOmTfLw8FB4eLgk6datWwoKClJgYKC+//57Zc+eXRMmTFDjxo116NAhubq6atq0aVqwYIHmz5+v8uXLa9q0afr666/1r3/967737dKliyIiIvTee++pUqVKioqK0oULF1S8eHGtXLlSbdu21fHjx+Xh4aGcOXNKksLCwvT5559r3rx58vf31/bt29W5c2cVLlxYdevW1W+//aY2bdooODhYvXr10t69ezVo0KBHen9SUlJUrFgxrVixQgULFtSOHTvUq1cv+fj46JVXXrF539zd3bV161ZFR0frtddeU8GCBfXuu++mqXYAQBZiAEAm0LVrV6Nly5aGYRhGSkqKER4ebri5uRmDBw+2Hvfy8jISExOt5yxatMgoV66ckZKSYt2XmJho5MyZ01i/fr1hGIbh4+NjTJ482Xr81q1bRrFixaz3MgzDqFu3rtG/f3/DMAzj+PHjhiQjPDz8nnVu2bLFkGRcvnzZuu/mzZtGrly5jB07dtiM7d69u9GhQwfDMAxj+PDhRkBAgM3xYcOGpbrW3/n5+RkzZsy47/G/Cw4ONtq2bWt93bVrV6NAgQLGtWvXrPvmzp1r5MmTx0hOTk5T7fd6ZgBA5kRyDyDTWL16tfLkyaNbt24pJSVFHTt2VGhoqPV4hQoVbObZHzx4UCdPnlTevHltrnPz5k2dOnVK8fHxOnv2rGrUqGE9lj17dlWvXj3V1Jy7IiMjlS1btnQl1idPntT169f1wgsv2OxPSkpSlSpVJEk///yzTR2SFBgYmOZ73M+cOXM0f/58xcTE6MaNG0pKSlLlypVtxlSqVEm5cuWyue/Vq1f122+/6erVqw+sHQCQddDcA8g06tevr7lz58rV1VW+vr7Knt32R1Tu3LltXl+9elXVqlXT4sWLU12rcOHCD1XD3Wk26XH16lVJ0po1a1S0aFGbY25ubg9VR1osXbpUgwcP1rRp0xQYGKi8efNqypQp2rVrV5qv4ajaAQDmoLkHkGnkzp1bZcuWTfP4qlWratmyZSpSpIg8PDzuOcbHx0e7du1SnTp1JEm3b9/Wvn37VLVq1XuOr1ChglJSUrRt2zY1bNgw1fG7f3OQnJxs3RcQECA3NzfFxMTcN/EvX7689cPBd+3cufPBD/kPfvzxR9WsWVNvvvmmdd+pU6dSjTt48KBu3Lhh/cVl586dypMnj4oXL64CBQo8sHYAQNbBajkAsqxOnTqpUKFCatmypb7//ntFRUVp69ateuutt/T7779Lkvr3769JkyZp1apVOnbsmN58881/XKO+ZMmS6tq1q15//XWtWrXKes3ly5dLkvz8/GSxWLR69WqdP39eV69eVd68eTV48GANHDhQCxcu1KlTp7R//369//77WrhwoSSpT58+OnHihIYMGaLjx49ryZIlWrBgQZqe848//lBkZKTNdvnyZfn7+2vv3r1av369fvnlF40aNUp79uxJdX5SUpK6d++un376SWvXrtWYMWPUt29fubi4pKl2AEDWQXMPIMvKlSuXtm/frhIlSqhNmzYqX768unfvrps3b1qT/EGDBunVV19V165drVNXWrdu/Y/XnTt3rl566SW9+eabevLJJ9WzZ09du3ZNklS0aFGNHTtWb7/9try8vNS3b19J0vjx4zVq1CiFhYWpfPnyaty4sdasWaNSpUpJkkqUKKGVK1dq1apVqlSpkubNm6eJEyem6TmnTp2qKlWq2Gxr1qxR79691aZNG7Vr1041atTQxYsXbVL8uxo0aCB/f3/VqVNH7dq104svvmjzWYYH1Q4AyDosxv0+VQYAAAAgSyG5BwAAAJwEzT0AAADgJGjuAQAAACdBcw8AAAA4CZp7AAAAwEnQ3AMAAABOguYeAAAAcBI09wAAAICToLkHAAAAnATNPQAAAOAkaO4BAAAAJ0FzDwAAADiJ/wNudQSfQOcwfgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Predict probabilities\n",
    "y_pred_probs = model.predict(X_test)\n",
    "\n",
    "# Convert probabilities to binary predictions\n",
    "y_pred = (y_pred_probs > 0.5).astype(int).flatten()\n",
    "\n",
    "# Compute confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "# Define class names for binary classification\n",
    "class_names = ['Class 0', 'Class 1']\n",
    "\n",
    "# Create a heatmap\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(cm, annot=True, cmap='Blues', fmt='d', xticklabels=class_names, yticklabels=class_names)\n",
    "plt.title('Confusion Matrix')\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.ylabel('True Label')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Saving the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sadeghi/.local/lib/python3.8/site-packages/keras/src/engine/training.py:3000: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  saving_api.save_model(\n"
     ]
    }
   ],
   "source": [
    "# Save the model in HDF5 format\n",
    "model.save(\"../6sbf_replayCheck_ResNet50.h5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import load_model\n",
    "\n",
    "# Load the HDF5 model\n",
    "model = load_model(\"../6sbf_replayCheck_ResNet50.h5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10 fold validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedKFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_splits = 10\n",
    "skf = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Metrics to store performance\n",
    "folds_evaluation = {}\n",
    "fold_accuracies = []\n",
    "\n",
    "for index in range(n_splits):\n",
    "    folds_evaluation[index] = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fold_counter = 0\n",
    "\n",
    "# 10-fold Cross Validation\n",
    "for train_index, val_index in skf.split(X_preprocessed, y):\n",
    "\n",
    "    # Split data\n",
    "    X_train, X_val = X_preprocessed[train_index], X_preprocessed[val_index]\n",
    "    y_train, y_val = y[train_index], y[val_index]\n",
    "    \n",
    "    # Train your model (assuming you have a model object `model`)\n",
    "    model.fit(X_train, y_train, epochs=100, batch_size=128)\n",
    "    \n",
    "    # Validate the model\n",
    "    # Accuracy, loss\n",
    "    val_loss, val_accuracy = model.evaluate(X_val, y_val, verbose=2)\n",
    "    fold_accuracies.append(val_accuracy)\n",
    "\n",
    "    # AUPR\n",
    "    y_pred_probs = model.predict(X_val)\n",
    "    y_test_onehot = to_categorical(y_val, num_classes=2)\n",
    "    aupr = calculate_aupr(y_test_onehot, y_pred_probs)\n",
    "\n",
    "    # AUC-ROC\n",
    "    auc_roc = calculate_auc_roc(y_val, y_pred_probs)\n",
    "    \n",
    "    # Saving the evaluation results\n",
    "    folds_evaluation[fold_counter][\"accuracy\"] =  val_accuracy\n",
    "    folds_evaluation[fold_counter][\"loss\"] =  val_loss\n",
    "    folds_evaluation[fold_counter][\"AUPR\"] =  aupr\n",
    "    folds_evaluation[fold_counter][\"AUC_ROC\"] = auc_roc\n",
    "    \n",
    "    fold_counter += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "folds_evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Average accuracy across folds\n",
    "average_accuracy = np.mean(fold_accuracies)\n",
    "print(\"10-Fold Cross-Validation Accuracy:\", average_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "folds_evaluation[\"average_accuracy\"] = average_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "# Save to a text file\n",
    "with open(\"../6sbf_replayCheck_Conv.txt\", \"w\") as file:\n",
    "    json.dump(folds_evaluation, file, indent=4) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.min(fold_accuracies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.max(fold_accuracies)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MobileNet model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.applications import MobileNet\n",
    "from tensorflow.keras.layers import Dense, GlobalAveragePooling2D\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.image import resize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_data(X, target_size=(32, 128)):\n",
    "    # Resize images to the target size\n",
    "    resized_images = np.array([resize(img, target_size).numpy() for img in X])\n",
    "    return resized_images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_mobilenet_model(input_shape):\n",
    "    # Load MobileNet with no top layer\n",
    "    base_model = MobileNet(include_top=False, weights=None, input_shape=input_shape)\n",
    "    \n",
    "    # Add global average pooling and fully connected layers\n",
    "    output = GlobalAveragePooling2D()(base_model.output)\n",
    "    output = Dense(256, activation='relu')(output)\n",
    "    output = Dense(128, activation='relu')(output)\n",
    "    output = Dense(1, activation='sigmoid', name='Similarity')(output)\n",
    "    model = Model(inputs=base_model.input, outputs=output)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preprocess data to match MobileNet input size\n",
    "input_shape = (32, 128, 3)  # Input size compatible with MobileNet\n",
    "X_preprocessed = preprocess_data(X, target_size=(32, 128))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the MobileNet model\n",
    "mobilenet_model = create_mobilenet_model(input_shape)\n",
    "\n",
    "# Compile the model\n",
    "mobilenet_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting train/test data (EKMs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting train and test data by proportion of 80/20\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Split the data into training and validation sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_preprocessed, y, test_size=0.2, random_state=42, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "3980/3980 [==============================] - 300s 74ms/step - loss: 0.2741 - accuracy: 0.8642 - val_loss: 0.1400 - val_accuracy: 0.9539\n",
      "Epoch 2/10\n",
      "3980/3980 [==============================] - 290s 73ms/step - loss: 0.1031 - accuracy: 0.9634 - val_loss: 0.0791 - val_accuracy: 0.9704\n",
      "Epoch 3/10\n",
      "3980/3980 [==============================] - 289s 73ms/step - loss: 0.0677 - accuracy: 0.9746 - val_loss: 0.0583 - val_accuracy: 0.9758\n",
      "Epoch 4/10\n",
      "3980/3980 [==============================] - 289s 73ms/step - loss: 0.0504 - accuracy: 0.9801 - val_loss: 0.0481 - val_accuracy: 0.9808\n",
      "Epoch 5/10\n",
      "3980/3980 [==============================] - 290s 73ms/step - loss: 0.0421 - accuracy: 0.9827 - val_loss: 0.0600 - val_accuracy: 0.9807\n",
      "Epoch 6/10\n",
      "3980/3980 [==============================] - 290s 73ms/step - loss: 0.0370 - accuracy: 0.9845 - val_loss: 0.0427 - val_accuracy: 0.9839\n",
      "Epoch 7/10\n",
      "3980/3980 [==============================] - 290s 73ms/step - loss: 0.0325 - accuracy: 0.9860 - val_loss: 0.0382 - val_accuracy: 0.9842\n",
      "Epoch 8/10\n",
      "3980/3980 [==============================] - 290s 73ms/step - loss: 0.0293 - accuracy: 0.9870 - val_loss: 0.0384 - val_accuracy: 0.9847\n",
      "Epoch 9/10\n",
      "3980/3980 [==============================] - 277s 70ms/step - loss: 0.0272 - accuracy: 0.9878 - val_loss: 0.0343 - val_accuracy: 0.9856\n",
      "Epoch 10/10\n",
      "3980/3980 [==============================] - 290s 73ms/step - loss: 0.0249 - accuracy: 0.9885 - val_loss: 0.0373 - val_accuracy: 0.9837\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x7f2a5bfc0040>"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the model\n",
    "mobilenet_model.fit(X_train, y_train, batch_size=32, epochs=10, validation_split=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = mobilenet_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accuracy, loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1244/1244 - 17s - loss: 0.0354 - accuracy: 0.9853 - 17s/epoch - 14ms/step\n",
      "Test Loss: 0.0354\n",
      "Test Accuracy: 0.9853\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model on the test set\n",
    "test_loss, test_accuracy = model.evaluate(X_test, y_test, verbose=2)\n",
    "print(f'Test Loss: {test_loss:.4f}')\n",
    "print(f'Test Accuracy: {test_accuracy:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### AUPR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_recall_curve, auc\n",
    "from tensorflow.keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_aupr(y_true, y_pred_probs):\n",
    "    \"\"\"\n",
    "    Calculate the Area Under the Precision-Recall Curve (AUPR).\n",
    "    \"\"\"\n",
    "    precision, recall, _ = precision_recall_curve(y_true.ravel(), y_pred_probs.ravel())\n",
    "    aupr = auc(recall, precision)\n",
    "    return aupr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1244/1244 [==============================] - 18s 14ms/step\n",
      "AUPR: 0.9990\n"
     ]
    }
   ],
   "source": [
    "y_pred_probs = model.predict(X_test)\n",
    "# y_test_onehot = to_categorical(y_test, num_classes=2)\n",
    "\n",
    "aupr = calculate_aupr(y_test, y_pred_probs)\n",
    "print(f\"AUPR: {aupr:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### AUC-ROC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "def calculate_auc_roc(y_true, y_pred_probs):\n",
    "    \"\"\"\n",
    "    Calculate the Area Under the Receiver Operating Characteristic Curve (AUC-ROC).\n",
    "    \"\"\"\n",
    "    auc_roc = roc_auc_score(y_true, y_pred_probs, multi_class='ovr')  # 'ovr' for one-vs-rest\n",
    "    return auc_roc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1244/1244 [==============================] - 18s 14ms/step\n",
      "AUC-ROC: 0.9992\n"
     ]
    }
   ],
   "source": [
    "y_pred_probs = model.predict(X_test)\n",
    "auc_roc = calculate_auc_roc(y_test, y_pred_probs)\n",
    "print(f\"AUC-ROC: {auc_roc:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "def calculate_confusion_matrix(y_true, y_pred):\n",
    "    \"\"\"\n",
    "    Calculate the confusion matrix.\n",
    "    \"\"\"\n",
    "    cm = confusion_matrix(y_true, y_pred)\n",
    "    return cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1244/1244 [==============================] - 18s 14ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Predict probabilities\n",
    "y_pred_probs = model.predict(X_test)\n",
    "\n",
    "# Convert probabilities to binary predictions\n",
    "y_pred = (y_pred_probs > 0.5).astype(int).flatten()\n",
    "\n",
    "# Compute confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "# Define class names for binary classification\n",
    "class_names = ['Class 0', 'Class 1']\n",
    "\n",
    "# Create a heatmap\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(cm, annot=True, cmap='Blues', fmt='d', xticklabels=class_names, yticklabels=class_names)\n",
    "plt.title('Confusion Matrix')\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.ylabel('True Label')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Saving the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the model in HDF5 format\n",
    "model.save(\"../6sbf_replayCheck_MobileNet.keras\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import load_model\n",
    "\n",
    "# Load the HDF5 model\n",
    "model = load_model(\"../6sbf_replayCheck_MobileNet.keras\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10 fold validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedKFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_splits = 10\n",
    "skf = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Metrics to store performance\n",
    "folds_evaluation = {}\n",
    "fold_accuracies = []\n",
    "\n",
    "for index in range(n_splits):\n",
    "    folds_evaluation[index] = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preprocess data to match MobileNet input size\n",
    "input_shape = (32, 128, 3)  # Input size compatible with MobileNet\n",
    "X_preprocessed = preprocess_data(X, target_size=(32, 128))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fold_counter = 0\n",
    "\n",
    "# 10-fold Cross Validation\n",
    "for train_index, val_index in skf.split(X_preprocessed, y):\n",
    "\n",
    "    # Split data\n",
    "    X_train, X_val = X_preprocessed[train_index], X_preprocessed[val_index]\n",
    "    y_train, y_val = y[train_index], y[val_index]\n",
    "\n",
    "    # Create the MobileNet model\n",
    "    model = create_mobilenet_model(input_shape)\n",
    "\n",
    "    # Compile the model\n",
    "    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "    \n",
    "    # Train your model (assuming you have a model object `model`)\n",
    "    model.fit(X_train, y_train, epochs=10, batch_size=128)\n",
    "    \n",
    "    # Validate the model\n",
    "    # Accuracy, loss\n",
    "    val_loss, val_accuracy = model.evaluate(X_val, y_val, verbose=2)\n",
    "    fold_accuracies.append(val_accuracy)\n",
    "\n",
    "    # AUPR\n",
    "    y_pred_probs = model.predict(X_val)\n",
    "    y_test_onehot = to_categorical(y_val, num_classes=2)\n",
    "    aupr = calculate_aupr(y_test_onehot, y_pred_probs)\n",
    "\n",
    "    # AUC-ROC\n",
    "    auc_roc = calculate_auc_roc(y_val, y_pred_probs)\n",
    "    \n",
    "    # Saving the evaluation results\n",
    "    folds_evaluation[fold_counter][\"accuracy\"] =  val_accuracy\n",
    "    folds_evaluation[fold_counter][\"loss\"] =  val_loss\n",
    "    folds_evaluation[fold_counter][\"AUPR\"] =  aupr\n",
    "    folds_evaluation[fold_counter][\"AUC_ROC\"] = auc_roc\n",
    "    \n",
    "    fold_counter += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "folds_evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Average accuracy across folds\n",
    "average_accuracy = np.mean(fold_accuracies)\n",
    "print(\"10-Fold Cross-Validation Accuracy:\", average_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "folds_evaluation[\"average_accuracy\"] = average_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "# Save to a text file\n",
    "with open(\"../6sbf_replayCheck_MobileNet.txt\", \"w\") as file:\n",
    "    json.dump(folds_evaluation, file, indent=4) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.min(fold_accuracies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.max(fold_accuracies)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MobileNet: v3 small"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.applications import MobileNetV3Small\n",
    "from tensorflow.keras.layers import Dense, GlobalAveragePooling2D\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.image import resize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_data(X, target_size=(32, 32)):\n",
    "    # Resize or pad images to the target size\n",
    "    resized_images = np.array([resize(img, target_size).numpy() for img in X])\n",
    "    return resized_images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_mobilenetv3small_model(input_shape=(32, 32, 3)):\n",
    "    # Input layer\n",
    "    inputs = Input(shape=input_shape)\n",
    "\n",
    "    # Base model (MobileNetV3Small) without pretrained weights\n",
    "    base_model = MobileNetV3Small(\n",
    "        input_shape=input_shape,\n",
    "        include_top=False,\n",
    "        weights=None  # No pretrained weights\n",
    "    )\n",
    "\n",
    "    # Pass the inputs through the base model\n",
    "    x = base_model(inputs, training=True)\n",
    "    \n",
    "    # Add custom classification layers\n",
    "    x = Flatten()(x)\n",
    "    x = Dense(128, activation='relu')(x)\n",
    "    x = Dense(64, activation='relu')(x)\n",
    "    outputs = Dense(1, activation='sigmoid', name='Similarity')(x)\n",
    "\n",
    "    # Create the model\n",
    "    model = Model(inputs, outputs)\n",
    "\n",
    "    # Compile the model\n",
    "    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = create_mobilenetv3small_model(input_shape=(32, 32, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_preprocessed = preprocess_data(X, target_size=(32, 32))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting train/test data (EKMs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting train and test data by proportion of 80/20\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Split the data into training and validation sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_preprocessed, y, test_size=0.2, random_state=42, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "3980/3980 [==============================] - 216s 50ms/step - loss: 0.3031 - accuracy: 0.8559 - val_loss: 0.2061 - val_accuracy: 0.9061\n",
      "Epoch 2/10\n",
      "3980/3980 [==============================] - 202s 51ms/step - loss: 0.1188 - accuracy: 0.9570 - val_loss: 0.1066 - val_accuracy: 0.9625\n",
      "Epoch 3/10\n",
      "3980/3980 [==============================] - 201s 51ms/step - loss: 0.0915 - accuracy: 0.9677 - val_loss: 0.0867 - val_accuracy: 0.9682\n",
      "Epoch 4/10\n",
      "3980/3980 [==============================] - 203s 51ms/step - loss: 0.0810 - accuracy: 0.9712 - val_loss: 0.0839 - val_accuracy: 0.9713\n",
      "Epoch 5/10\n",
      "3980/3980 [==============================] - 203s 51ms/step - loss: 0.0742 - accuracy: 0.9735 - val_loss: 0.0779 - val_accuracy: 0.9723\n",
      "Epoch 6/10\n",
      "3980/3980 [==============================] - 201s 51ms/step - loss: 0.0686 - accuracy: 0.9754 - val_loss: 0.0715 - val_accuracy: 0.9752\n",
      "Epoch 7/10\n",
      "3980/3980 [==============================] - 201s 50ms/step - loss: 0.0653 - accuracy: 0.9765 - val_loss: 0.0685 - val_accuracy: 0.9753\n",
      "Epoch 8/10\n",
      "3980/3980 [==============================] - 201s 51ms/step - loss: 0.0619 - accuracy: 0.9774 - val_loss: 0.0663 - val_accuracy: 0.9763\n",
      "Epoch 9/10\n",
      "3980/3980 [==============================] - 201s 50ms/step - loss: 0.0591 - accuracy: 0.9781 - val_loss: 0.0659 - val_accuracy: 0.9756\n",
      "Epoch 10/10\n",
      "3980/3980 [==============================] - 200s 50ms/step - loss: 0.0567 - accuracy: 0.9790 - val_loss: 0.0704 - val_accuracy: 0.9770\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x7f2a5bfc0a30>"
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the model\n",
    "model.fit(X_train, y_train, batch_size=32, epochs=10, validation_split=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accuracy, loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1244/1244 - 23s - loss: 0.0662 - accuracy: 0.9778 - 23s/epoch - 19ms/step\n",
      "Test Loss: 0.0662\n",
      "Test Accuracy: 0.9778\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model on the test set\n",
    "test_loss, test_accuracy = model.evaluate(X_test, y_test, verbose=2)\n",
    "print(f'Test Loss: {test_loss:.4f}')\n",
    "print(f'Test Accuracy: {test_accuracy:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### AUPR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_recall_curve, auc\n",
    "from tensorflow.keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_aupr(y_true, y_pred_probs):\n",
    "    \"\"\"\n",
    "    Calculate the Area Under the Precision-Recall Curve (AUPR).\n",
    "    \"\"\"\n",
    "    precision, recall, _ = precision_recall_curve(y_true.ravel(), y_pred_probs.ravel())\n",
    "    aupr = auc(recall, precision)\n",
    "    return aupr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1244/1244 [==============================] - 27s 21ms/step\n",
      "AUPR: 0.9976\n"
     ]
    }
   ],
   "source": [
    "y_pred_probs = model.predict(X_test)\n",
    "# y_test_onehot = to_categorical(y_test, num_classes=2)\n",
    "\n",
    "aupr = calculate_aupr(y_test, y_pred_probs)\n",
    "print(f\"AUPR: {aupr:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### AUC-ROC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "def calculate_auc_roc(y_true, y_pred_probs):\n",
    "    \"\"\"\n",
    "    Calculate the Area Under the Receiver Operating Characteristic Curve (AUC-ROC).\n",
    "    \"\"\"\n",
    "    auc_roc = roc_auc_score(y_true, y_pred_probs, multi_class='ovr')  # 'ovr' for one-vs-rest\n",
    "    return auc_roc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   1/1244 [..............................] - ETA: 44s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1244/1244 [==============================] - 26s 21ms/step\n",
      "AUC-ROC: 0.9976\n"
     ]
    }
   ],
   "source": [
    "y_pred_probs = model.predict(X_test)\n",
    "auc_roc = calculate_auc_roc(y_test, y_pred_probs)\n",
    "print(f\"AUC-ROC: {auc_roc:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "def calculate_confusion_matrix(y_true, y_pred):\n",
    "    \"\"\"\n",
    "    Calculate the confusion matrix.\n",
    "    \"\"\"\n",
    "    cm = confusion_matrix(y_true, y_pred)\n",
    "    return cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1244/1244 [==============================] - 24s 19ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Predict probabilities\n",
    "y_pred_probs = model.predict(X_test)\n",
    "\n",
    "# Convert probabilities to binary predictions\n",
    "y_pred = (y_pred_probs > 0.5).astype(int).flatten()\n",
    "\n",
    "# Compute confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "# Define class names for binary classification\n",
    "class_names = ['Class 0', 'Class 1']\n",
    "\n",
    "# Create a heatmap\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(cm, annot=True, cmap='Blues', fmt='d', xticklabels=class_names, yticklabels=class_names)\n",
    "plt.title('Confusion Matrix')\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.ylabel('True Label')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Saving the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the model in HDF5 format\n",
    "model.save(\"../6sbf_replayCheck_MobileNetV3Small.keras\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import load_model\n",
    "\n",
    "# Load the HDF5 model\n",
    "model = load_model(\"../6sbf_replayCheck_MobileNetV3Small.keras\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10 fold validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedKFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_splits = 10\n",
    "skf = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Metrics to store performance\n",
    "folds_evaluation = {}\n",
    "fold_accuracies = []\n",
    "\n",
    "for index in range(n_splits):\n",
    "    folds_evaluation[index] = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "4478/4478 [==============================] - 245s 51ms/step - loss: 0.2882 - accuracy: 0.8705 - val_loss: 2.6639 - val_accuracy: 0.5983\n",
      "Epoch 2/10\n",
      " 405/4478 [=>............................] - ETA: 3:02 - loss: 0.1356 - accuracy: 0.9500"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[328], line 18\u001b[0m\n\u001b[1;32m     15\u001b[0m model\u001b[38;5;241m.\u001b[39mcompile(optimizer\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124madam\u001b[39m\u001b[38;5;124m'\u001b[39m, loss\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbinary_crossentropy\u001b[39m\u001b[38;5;124m'\u001b[39m, metrics\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maccuracy\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m     17\u001b[0m \u001b[38;5;66;03m# Train your model (assuming you have a model object `model`)\u001b[39;00m\n\u001b[0;32m---> 18\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m32\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalidation_split\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.2\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     20\u001b[0m \u001b[38;5;66;03m# Validate the model\u001b[39;00m\n\u001b[1;32m     21\u001b[0m \u001b[38;5;66;03m# Accuracy, loss\u001b[39;00m\n\u001b[1;32m     22\u001b[0m val_loss, val_accuracy \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mevaluate(X_val, y_val, verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m)\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/keras/src/utils/traceback_utils.py:65\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     63\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     64\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 65\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     66\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m     67\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/keras/src/engine/training.py:1742\u001b[0m, in \u001b[0;36mModel.fit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1734\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m tf\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mexperimental\u001b[38;5;241m.\u001b[39mTrace(\n\u001b[1;32m   1735\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   1736\u001b[0m     epoch_num\u001b[38;5;241m=\u001b[39mepoch,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1739\u001b[0m     _r\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m   1740\u001b[0m ):\n\u001b[1;32m   1741\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_begin(step)\n\u001b[0;32m-> 1742\u001b[0m     tmp_logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1743\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m data_handler\u001b[38;5;241m.\u001b[39mshould_sync:\n\u001b[1;32m   1744\u001b[0m         context\u001b[38;5;241m.\u001b[39masync_wait()\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/tensorflow/python/util/traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 150\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    152\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:825\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    822\u001b[0m compiler \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxla\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnonXla\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    824\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OptionalXlaContext(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile):\n\u001b[0;32m--> 825\u001b[0m   result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    827\u001b[0m new_tracing_count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexperimental_get_tracing_count()\n\u001b[1;32m    828\u001b[0m without_tracing \u001b[38;5;241m=\u001b[39m (tracing_count \u001b[38;5;241m==\u001b[39m new_tracing_count)\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:857\u001b[0m, in \u001b[0;36mFunction._call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    854\u001b[0m   \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[1;32m    855\u001b[0m   \u001b[38;5;66;03m# In this case we have created variables on the first call, so we run the\u001b[39;00m\n\u001b[1;32m    856\u001b[0m   \u001b[38;5;66;03m# defunned version which is guaranteed to never create variables.\u001b[39;00m\n\u001b[0;32m--> 857\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_no_variable_creation_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# pylint: disable=not-callable\u001b[39;00m\n\u001b[1;32m    858\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_variable_creation_fn \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    859\u001b[0m   \u001b[38;5;66;03m# Release the lock early so that multiple threads can perform the call\u001b[39;00m\n\u001b[1;32m    860\u001b[0m   \u001b[38;5;66;03m# in parallel.\u001b[39;00m\n\u001b[1;32m    861\u001b[0m   \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/tensorflow/python/eager/polymorphic_function/tracing_compiler.py:148\u001b[0m, in \u001b[0;36mTracingCompiler.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    145\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n\u001b[1;32m    146\u001b[0m   (concrete_function,\n\u001b[1;32m    147\u001b[0m    filtered_flat_args) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_maybe_define_function(args, kwargs)\n\u001b[0;32m--> 148\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mconcrete_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_flat\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    149\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfiltered_flat_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcaptured_inputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconcrete_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcaptured_inputs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/tensorflow/python/eager/polymorphic_function/monomorphic_function.py:1349\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[0;34m(self, args, captured_inputs)\u001b[0m\n\u001b[1;32m   1345\u001b[0m possible_gradient_type \u001b[38;5;241m=\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[1;32m   1346\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (possible_gradient_type \u001b[38;5;241m==\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[1;32m   1347\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m executing_eagerly):\n\u001b[1;32m   1348\u001b[0m   \u001b[38;5;66;03m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[0;32m-> 1349\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_build_call_outputs(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_inference_function\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m   1350\u001b[0m forward_backward \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[1;32m   1351\u001b[0m     args,\n\u001b[1;32m   1352\u001b[0m     possible_gradient_type,\n\u001b[1;32m   1353\u001b[0m     executing_eagerly)\n\u001b[1;32m   1354\u001b[0m forward_function, args_with_tangents \u001b[38;5;241m=\u001b[39m forward_backward\u001b[38;5;241m.\u001b[39mforward()\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/tensorflow/python/eager/polymorphic_function/atomic_function.py:196\u001b[0m, in \u001b[0;36mAtomicFunction.__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m    194\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m record\u001b[38;5;241m.\u001b[39mstop_recording():\n\u001b[1;32m    195\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mexecuting_eagerly():\n\u001b[0;32m--> 196\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_bound_context\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    197\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    198\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    199\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunction_type\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mflat_outputs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    200\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    201\u001b[0m   \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    202\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m make_call_op_in_graph(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28mlist\u001b[39m(args))\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/tensorflow/python/eager/context.py:1457\u001b[0m, in \u001b[0;36mContext.call_function\u001b[0;34m(self, name, tensor_inputs, num_outputs)\u001b[0m\n\u001b[1;32m   1455\u001b[0m cancellation_context \u001b[38;5;241m=\u001b[39m cancellation\u001b[38;5;241m.\u001b[39mcontext()\n\u001b[1;32m   1456\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cancellation_context \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1457\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m \u001b[43mexecute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1458\u001b[0m \u001b[43m      \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mutf-8\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1459\u001b[0m \u001b[43m      \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1460\u001b[0m \u001b[43m      \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtensor_inputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1461\u001b[0m \u001b[43m      \u001b[49m\u001b[43mattrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1462\u001b[0m \u001b[43m      \u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1463\u001b[0m \u001b[43m  \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1464\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1465\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute_with_cancellation(\n\u001b[1;32m   1466\u001b[0m       name\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m   1467\u001b[0m       num_outputs\u001b[38;5;241m=\u001b[39mnum_outputs,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1471\u001b[0m       cancellation_manager\u001b[38;5;241m=\u001b[39mcancellation_context,\n\u001b[1;32m   1472\u001b[0m   )\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/tensorflow/python/eager/execute.py:53\u001b[0m, in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     51\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m     52\u001b[0m   ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[0;32m---> 53\u001b[0m   tensors \u001b[38;5;241m=\u001b[39m \u001b[43mpywrap_tfe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTFE_Py_Execute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_handle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     54\u001b[0m \u001b[43m                                      \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     55\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m     56\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "fold_counter = 0\n",
    "\n",
    "# 10-fold Cross Validation\n",
    "for train_index, val_index in skf.split(X_preprocessed, y):\n",
    "\n",
    "    # Split data\n",
    "    X_train, X_val = X_preprocessed[train_index], X_preprocessed[val_index]\n",
    "    y_train, y_val = y[train_index], y[val_index]\n",
    "\n",
    "    # Create the MobileNet model\n",
    "    model = create_mobilenetv3small_model(input_shape)\n",
    "\n",
    "    # Compile the model\n",
    "    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "    \n",
    "    # Train your model (assuming you have a model object `model`)\n",
    "    model.fit(X_train, y_train, epochs=10, batch_size=32, validation_split=0.2)\n",
    "    \n",
    "    # Validate the model\n",
    "    # Accuracy, loss\n",
    "    val_loss, val_accuracy = model.evaluate(X_val, y_val, verbose=2)\n",
    "    fold_accuracies.append(val_accuracy)\n",
    "\n",
    "    # AUPR\n",
    "    y_pred_probs = model.predict(X_val)\n",
    "    # y_test_onehot = to_categorical(y_val, num_classes=2)\n",
    "    aupr = calculate_aupr(y_val, y_pred_probs)\n",
    "\n",
    "    # AUC-ROC\n",
    "    auc_roc = calculate_auc_roc(y_val, y_pred_probs)\n",
    "    \n",
    "    # Saving the evaluation results\n",
    "    folds_evaluation[fold_counter][\"accuracy\"] =  val_accuracy\n",
    "    folds_evaluation[fold_counter][\"loss\"] =  val_loss\n",
    "    folds_evaluation[fold_counter][\"AUPR\"] =  aupr\n",
    "    folds_evaluation[fold_counter][\"AUC_ROC\"] = auc_roc\n",
    "    \n",
    "    fold_counter += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: {}, 1: {}, 2: {}, 3: {}, 4: {}, 5: {}, 6: {}, 7: {}, 8: {}, 9: {}}"
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "folds_evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Average accuracy across folds\n",
    "average_accuracy = np.mean(fold_accuracies)\n",
    "print(\"10-Fold Cross-Validation Accuracy:\", average_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "folds_evaluation[\"average_accuracy\"] = average_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "# Save to a text file\n",
    "with open(\"../6sbf_replayCheck_MobileNetV3Small.txt\", \"w\") as file:\n",
    "    json.dump(folds_evaluation, file, indent=4) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.min(fold_accuracies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.max(fold_accuracies)"
   ]
  }
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